VS101-2023.pdf

Table Of Contents
Video Surveillance History...........................................................................................1
Video Surveillance Architecture 101..........................................................................14
Video Surveillance Cameras 101................................................................................27
VMS 101....................................................................................................................50
Video Analytics 101...................................................................................................81
Facial Recognition 101.............................................................................................101
Surveillance Storage 101.........................................................................................122
VSaaS 101................................................................................................................138
Video Surveillance Business 101..............................................................................153
Video Surveillance Trends 101 ................................................................................163

Copyright IPVM 1
Video Surveillance History
The video surveillance market has changed significantly since 2000, going from VCRs
to now AI and the cloud.
The goal of this history is to help professionals understand the important business
and technology shifts that impact the market today, including:
• 2000 - 2005 DVR Era
• 2001 - 9/11 Impact
• 2006 - Infancy IP and VMS
• 2008 - 2012 MP Cameras Go H.264
• 2009 - 2013 Cloud Hype / Bursts
• 2010 - 2018 Struggles For Video Analytics
• 2012 - 2014 Rise and Fall of Edge Storage
• 2010s WDR and Low Light Improvements
• 2015 Smart CODECs Rise
• 2018 H.265 Mainstream
• Storage No Longer Major Problem
• Slowing of Camera Resolution Increases

Copyright IPVM 2
• HD Analog Rises 2014, Niche Now
• 2015 - Now Rise Cybersecurity
• 2013 - 2017 Rise of PRC Manufacturers
• 2015 - 2017 Race To The Bottom
• 2018 - Now The West vs PRC
• 2019 - Rise AI and Cloud Startups
• 2019 - Now Hostage-as-a-Service Systems
• 2020 - Facial Recognition Negativity
• 2020 - 2022 Coronavirus Impact
• 2021 - Now AI and Cloud Mainstream
2000 - 2005, DVR Era
The first part of the 2000s witnessed the rise of DVRs, replacing VCRs, bringing two
important advances - (1) replacing costly and cumbersome VHS tapes with digital
recording and (2) enabling monitoring of video surveillance over IP networks.
Recorders were quite expensive in that era, with $5,000 to $10,000 for a 16-channel
appliance common, even with limited storage and low resolution (CIF, a fraction of
even SD, was widespread). However, it was less expensive than the operational
costs of maintaining VHS tapes plus had the benefit that video could be viewed
throughout an organization's facilities. Remote viewing over the Internet was
possible but given limited bandwidth (max WAN bandwidth of 1Mb/s to 3Mb/s was
frequent) and limited CODECs (this was before the rise of H.264) meant that the
quality and speed of Internet-based video surveillance watching was poor.

Copyright IPVM 3
2001, 9/11 Impact
Along with technology rapidly improving in the late 1990s to
early 2000s, terrorism in the early 2000s, most notably 9/11, drove increasing
demand for video surveillance.
Some of this was good but some bad. On the positive side, it increased awareness
and interest in considering emerging technology. And for sellers, it was clearly a
boon as the fear of being the next target of terrorism made it easy to justify
spending on these systems. On the negative side, many purchases were rashly
made on technology that was not mature enough, which resulted in, at best,
security theater, and, at worst, a waste of money. This has parallels with the 2020
coronavirus fever camera response.
2006, Infancy IP and VMS
By 2006, the industry was dominated by DVRs and SD analog cameras.
VMS software and IP cameras were still niches. Some megapixel cameras were
offered but they were far more expensive than analog ones and only supported
MJPEG encoding, making the storage and transmission of these cameras even more
expensive.
Analytics was fairly 'hot' in 2006, driven by its potential and VC funding, though with
very limited deployments.
The major players were generally Western and Japanese large manufacturers, with
Chinese branded sales nearly non-existent in the West (Dahua and Hikvision were
mostly unknown) and notable companies today like Axis, Milestone, and Genetec
still relative 'startups' (indeed, Avigilon only started selling commercially in 2007).

Copyright IPVM 4
2008 - 2012, MP Cameras Go H.264
The single biggest driver for IP was the adoption of H.264 for MP
cameras. This drove mainstream IP camera deployment and, by extension, VMS
software. With MP H.264, IP was able to deliver clear benefits in resolution with
reasonable increases in total costs (compared to the earlier MJPEG only MP era).
2009 – 2013, Cloud Hype / Bursts
Along with the rise of MP / IP cameras came a significant interest
in connecting those cameras to the cloud. The hope was that it would eliminate on-
site recording, on-site maintenance, etc. Bandwidth limitations and poor cloud VMS
capabilities doomed this. It never really gained much market share and with EMC
dumping Axis, it marked the end of that era / error.
It would take many years for the cloud to re-emerge as a significant player within
video surveillance.
2008 - 2018, Struggles For Video Analytics
Video analytics never went mainstream, marred by performance
problems and unhappy customers. 2011, with ObjectVideo (OV) suing Bosch,
Samsung and Sony confirmed and deepened the problems of video analytics, with
OV, one of the most well-funded analytics companies effectively ending commercial
sales and suing the industry. OV essentially won, with Avigilon paying nearly $80
million for ObjectVideo patents in 2014. The industry lost, though, as analytics
remained a niche offering with minimal industry investment.

Copyright IPVM 5
2012 - 2014, Rise and Fall of Edge Storage
For a few years, many saw edge storage as being a potential next
big thing but it remained a niche. The promise of edge storage was
to eliminate NVRs / recorder appliances as the storage and software could be
deployed inside the IP camera. Reliability problems hurt early adopters. And the rise
of low-cost PRC China NVRs (a few hundred dollars is now commonplace), as well as
HD analog for even less (see below), pushed edge storage as more of a niche
providing redundancy for higher-end applications.
WDR and Low Light Improvements
Cameras have become much better at handling challenging imaging conditions,
especially harsh lighting and darkness. A decade ago, WDR cameras were fairly
limited and expensive (this was when Pixim was considered leading edge). Low light
performance was generally poor. And these problems were even worse for MP
cameras where real WDR was essentially non-existent and low light performance
was often terrible. The state of the art in our WDR Shootout 2011 is nothing
compared to even 'average' true WDR cameras today.
2015, Smart CODECs Rise
One of the biggest changes in the last 6 years has been the rise of 'Smart CODECs'
that regularly delivers 50% bandwidth reductions vs 'un-smart' codecs by
dynamically adjusting the compression and I frame intervals based on analyzing the
scene. Smart CODECs are independent of H.264 or H.265 and can be used with
either. For the first few years of their introduction, they were primarily used with
H.264 but are now generally used with H.265 as well.

Copyright IPVM 6
2018, H.265 Mainstream
While smart codecs reduced the benefit of adding H.265 (by delivering bandwidth
savings with 'old' H.264), by 2018, almost all manufacturers were releasing new
cameras supporting H.265 + smart codecs.
Storage No Longer Major Problem
While there are certainly cases where storage is still a major challenge, the
combination of smart codecs, H.265, and increased hard drive storage / price ratio
has made video surveillance storage much less of an issue than ever before. In the
2000s and even the first half of 2010s, storage was a challenge, as much smaller
hard drives, less efficient compression, and burgeoning HD resolution demand made
the cost and complexity of storage a major factor. Now, storage is generally a
simpler matter.
Slowing of Camera Resolution Increases
Slowing of Camera Resolution Increases
Contributing to that is camera resolution increases are slowing. In the 2008 - 2013
era, resolution (specifically pixel count) was roughly quadrupling from SD (~0.3MP)
to 1.3MP. Now, resolution is continuing to increase, but on average, more in the 3
to 6MP range, which is a much slower rate of increase than in the 2005 - 2015 era.
From our resolution usage statistics report, this chart show the trend over the past 8
years:

Copyright IPVM 7
While 3MP to 5MP cameras have become the majority, usage of 8MP and higher
cameras are still a distinct minority. Moreover, there are very few cameras over
12MP even being sold today and while some talk about 8K (i.e. 33MP), such video
surveillance cameras are still mostly conceptual.
2014 HD Analog Rises, Niche Now
While SD analog took a long time to 'die', it was finally killed off
by HD analog in ~2015. For more than a decade, IP was the only practical way to
deliver MP / HD. But early in the 2010s, HD analog, which transmits over coaxial
cable just like NTSC / PAL, emerged. It has wiped out SD analog, becoming a player
in home / SMB kit sales and in the low to mid market.
In the past few years, HD analog adoption relative to IP has slowed. While HD
analog has increased its maximum resolution to 8MP and has added (limited) Power
over Coax, even HD analog manufacturers have favored marketing their IP offerings,
mainly relegating HD analog to the most cost-sensitive applications and
geographies. See our HD Analog vs IP Guide

Copyright IPVM 8
2014- 2020, Rise Cybersecurity
While cybersecurity remains a significant consideration, reduced
usage of PRC-manufactured products in light of heightened US and European
scrutiny and restrictions has begun to decrease cybersecurity prominence.
Previously, notable cybersecurity issues came from easily exploitable backdoors of
the industry's largest manufacturers - Dahua backdoor and Hikvision backdoor -
with the Dahua backdoor resulting in mass hacks in 2017, as well as critical
vulnerabilities in 2021 for both Dahua and Hikvision. IPVM maintains a directory of
cybersecurity vulnerabilities for video surveillance products.
The other major element of cybersecurity is the risk of state-sponsored or state-
controlled companies. This first emerged as a practical issue in 2016 when Genetec
expelled Hikvision and Huawei saying they were security risks.
This has certainly increased as the US government has banned the use of those
products as well as Dahua.
2013 - 2017, Rise of PRC Manufacturers
Even in 2012, PRC manufacturers had a negligible market
share in branded Western sales. For example, see our 2010 Hikvision IP camera test
to see how bad they were back then. Indeed, Hikvision saw Western direct branded
sales as a 'dream' in 2009.
While PRC manufacturers had, for many years, been OEM suppliers to Western
brands, it has only been since 2013 when PRC-branded sales exploded in the West.
Before PRC manufacturers expanded in the Western market, $300 was considered low
cost for IP cameras, now $100 (or less) MP cameras are commonplace. In particular,
Hikvision has also been very aggressive about offering across the board price cuts

Copyright IPVM 9
monthly, something previously very rare in the industry. These moves combined have
resulted in a significant ongoing shift to PRC brands.
2015 - 2017, Race To The Bottom
This led to the 'race to the bottom', as manufacturers kept cutting prices, some to
gain share (e.g., Hikvision) and others simply to stay alive.
The race to the bottom has now ended, due to a combination of less effective price
cuts with prices having gotten so low, rising local costs as PRC entrants expanded,
cybersecurity issues (such as the backdoors), criticism / backlash and now the US
government ban.
2018 To Current, The West vs PRC
In 2018, the US government passed a law to ban US government use and funding for
Dahua, Hikvision, and Huawei products. While the primary effect was obviously
within the US, this move has increased scrutiny of these PRC manufacturers
elsewhere in the West, including the EU Parliament removing Hikvision, citing
human rights abuses. This has led to falling Hikvision sales in Europe and North
America. See our directory of Hikvision global news reports for more examples.
Moreover, the US government's move to sanction Huawei and repeated reports that
Dahua and Hikvision are being considered for sanctions for their billion dollars of
contracts in Xinjiang, where a million people are held in concentration camps, could
bring further changes to the video surveillance market.
In 2022, further action occurred, including the UK government banning PRC
surveillance equipment in sensitive government facilities and the US FCC enacting a
prohibition on new product authorizations for Dahua and Hikvision.

Copyright IPVM 10
The current trend is for increased conflict between the West and the PRC, though
shifts are possible in the future.
2019, Rise AI and Cloud Startups
While politics has become a major factor in video surveillance, recently, there has
been an emergence of a record number of video surveillance startups.
AI and cloud drove this. While the previous eras were driven by increases in
resolution and price decreases, this era is being far more shaped by software to
analyze video using 'deep learning' and to manage video in the cloud. Of course, video
analytics and cloud have been around for more than a decade. The difference in 2019
is that a host of companies capitalized on improvements in analytics and maturity of
supporting technologies around cloud (bandwidth availability, cloud infrastructure,
etc.).
2019 To Current, Hostage-as-a-Service
For the last 2 decades, open systems have been predominant in video surveillance,
whether via NTSC/PAL in analog or ONVIF in IP.
Now, a new wave of entrants, typified by Verkada, Meraki, and Rhombus, have
gained traction with closed, locked in systems, that require buying products and
then paying an ongoing subscription for the product to work at all. The largest
company in this segment, Verkada, was valued at $3.2 billion after a 2022 venture-
backed round.

Copyright IPVM 11
These companies have seen success so
far, adding a fundamental change to how
video surveillance is sold and managed.
While this fuels the financial results of
these providers, we believe this is a net
negative for both users and the industry
as holding users hostage restricts choice and
reduces options in connecting other cameras or systems.
2020 To Current, Fight Facial Recognition
Criticism of facial recognition has increased significantly in
many parts of the world, including the US, adding to concerns
that many Europeans have had. This criticism over the bias of
facial recognition and its misuse has lead to calls for its regulation and even for it to
be outlawed in an increasing number of areas. Related, The US Fight Over Facial
Recognition Explained.
This has caused many facial recognition companies to face challenges, including
layoffs at Anyvision (now Oosto) and at FaceFirst. Indeed, Anyvision shifted focus
away from video surveillance towards access control to navigate these challenges.
A secondary, but still important issue, is how the wearing of masks have
undermined the performance of facial recognition, especially in video surveillance.
On the plus side, algorithms are improving and, at some point, mask wearing will
decline, but challenges for facial recognition are significant, for the time being.
Click here to view the video on IPVM

Copyright IPVM 12
2020 To 2022, Coronavirus Impact
As with the entire world, coronavirus significantly impacted video
surveillance, including:
• Net revenue declined for the year, as lockdowns hampered buying and
installing products. For example, integrators were hit fairly hard but began to
recover at yearend.
• A surge in fever camera purchases in mid-2020. The hottest selling video
surveillance item of 2020 was fever camera/screening. The problem was
that many of these devices are rigged and health authorities have
increasingly warned about their inefficiency. Despite this, companies who
made or sold them did much better financially than those who did not. This
trend declined sharply beginning in 2021.
• Cloud systems benefited as the increase in work from home and remote
monitoring increased the importance of being able to manage and access
systems from anywhere.
As global restrictions continue to ease, pandemic-era fever camera sales have
plummeted and supply chain problems have continued to improve. Cloud offerings,
however, have continued to gain ground.
2020 to Current, AI and Cloud Mainstream
AI and Cloud have moved beyond niches within video surveillance to become widely
adopted by even the largest and most incumbent of competitors. While a few years
ago it was commonplace for incumbents to not provide AI or cloud, the opposite is
now the case.

Copyright IPVM 13
The use of AI and the cloud are now fundamentally reshaping how video
surveillance is sold, deployed, and used.

Copyright IPVM 14
Video Surveillance Architecture 101
Video surveillance can be designed and deployed in a number of ways. This 101
examines the most common options and architectures used in 2023.
Architectures are based on 5 fundamental decisions:
• Encoding - Where is the video encoded? Camera? Encoder? Recorder?
• Storage - Where is the video stored? Camera? Recorder? SAN? NAS? Cloud?
• Analytics - Where is the video analyzed? Camera? Recorder? Server? Cloud?
• Management - Where is the video managed? Camera? Recorder? VMS?
Cloud?
• Monitor - Where is the video monitored? PC / workstation? PVM? Video wall?
Phone?

Copyright IPVM 15
In this chapter, we explain the tradeoffs of these 5 fundamental decisions, overview
a number of the most common combinations, and what future trends are most
promising.
The chart below overviews the options for each area/decision:

Copyright IPVM 16
Encoding
All surveillance video is encoded (note, this is arguably not technically true but if you
know this, you should not be reading a 101 guide).
Surveillance video is going to be encoded, i.e., the native analog signal will need to
be digitized such that it can be displayed, analyzed, and transmitted amongst
computers.
The tradeoffs of the 3 typical locations are:
• Camera: Encoding in the camera increases costs relative to analog cameras.
Also, encoding in a camera decreases the per-link maximum transmission
distance of the video signal. However, encoding in the camera supports
analytics and advanced features because there is essentially a full computer
onboard.
• Encoder: Allows the use of any analog camera with a VMS or recorder.
Encoders also spread out the cost of additional encoding hardware costs over
multiple cameras. Encoders require a separate recorder for video storage.
• Recorder: Provides a single box for encoding and recording. All cameras must
be directly cabled, or home run, to the recorder for encoding, which can

Copyright IPVM 17
increase costs of installation. This is more likely to be appealing in small
systems.
Storage
Surveillance video must be stored in order to review past events. Encoded video
streams are saved as video files, where they can be selected and played back in client
decoding software. Storage can be located in cameras, recorders, network based, or
in the cloud.
• Camera: Storing video in a camera can eliminate the need for a separate
recorder. However, recording storage is limited based on the storage type
supported (e.g. microSD, SSD, Flash). The cost per Byte is higher than using a
recorder. Additionally, if a camera is damaged or stolen, loss of recorded
video is likely.
• Recorder: Storing video in a recorder can spread the cost of storage across
multiple cameras, which reduces the cost per byte compared to camera
storage. Also, this can add options for storage redundancy by using RAID.

Copyright IPVM 18
However, isolating storage in non-centralized recorders is inefficient and risks
significant video loss.
• NAS: Storing video in a NAS is a low-cost option for network-attached storage
for small-scale systems. Storage speed and redundancy are lower than
Recorder or SAN-based systems.
• SAN: Storing video in a SAN offers large-scale expansion and supports flexible
design architectures. Adding secondary hardware for storage will typically
significantly increase costs when compared to adding more storage in the
primary recorder.
• Cloud: Storing video in the Cloud decreases (or eliminates) the need for
recorder storage. However, Internet upload use increases and video
recordings can be lost during an Internet outage. There are also practical
limitations to the number of cameras that can upload video to the cloud,
based on Internet bandwidth. Once the video is uploaded to the cloud, it can
typically be viewed directly without connecting to the local cameras/gateway.
Analytics
Surveillance video can be analyzed by software to determine if it contains motion, a
person, vehicle, recognizes a face, reads a license plate, etc.
Analytic information is associated with the video stream and can be used to display
bounding boxes, trigger alarms, or is saved to increase efficiency when searching for
video.

Copyright IPVM 19
• Camera: Camera-based analytics can be more accurate because they can be
performed on low-compression, high-quality video, before encoding.
However, powerful processors required for accurate analytics have typically
been limited to expensive cameras and/or specialized capabilities (e.g.
LPR/ANPR).
• Recorder: Analyzing video on a recorder generally allows for a more powerful
processor than a single camera could offer, which is used for all cameras
connected to the recorder. Also, the analytics are immediately integrated with
the viewing software. However, recorder manufacturers do not want to over-
specify the hardware to keep the recorder's price low, and often the analytics
are limited.
• Server: Dedicated analytics appliances are built to have increased analytics
processing than Recorders, and typically include higher-spec components.
However, server-based analytics add significant cost and can be noisy and
power-intensive.
• Cloud: Analyzing video in the Cloud should eliminate the resource limitations
of recorders or servers, and provide high-accuracy analytics. Cloud processing

Copyright IPVM 20
can be expensive, and cloud-based analytics commonly sell for $25-$50 per
month. Internet upload speeds may be a bottleneck, limiting the number of
cameras that can be transmitted and analyzed. Hybrid Camera-Cloud: Some
VSaaS perform people and vehicle detection on camera and use Cloud
processing for advanced analytics (facial recognition, LPR, Appearance
Search). Many VSaaSes include/bundle these for no additional cost with the
basic subscription package. Hybrid analytics can mitigate Internet upload
increases by only transmitting still images of people and vehicles detected on-
premise, rather than continuous video.
Management
Surveillance system management ensures that only approved users can view video
and it is stored for the correct number of days. It can also monitor camera
configuration and system health.
• Camera: Camera-based management provides full system setup without
purchasing any servers or software. Additionally, camera manufacturers
typically provide a free software configuration tool for managing settings.

Copyright IPVM 21
There are cost savings to not purchasing a recorder/VMS, but this is
uncommon outside of very small systems due to increased complexity.
• Recorder: This offers decreased management complexity by supporting
multiple cameras through a single box, typically at a lower cost than VMS.
Recorders may also have feature limitations compared to VMS. Multi-recorder
systems typically require each server to be managed as a standalone system
or using a VMS client to connect to each recorder.
• VMS: This offers a single point of management across multiple
servers/recorders, often with a main management or directory server. Some
VMSes also offer management redundancy, supporting backup servers if the
main management server fails. This comes with an increased cost compared
to standalone recorders.
• Cloud: This typically offers central global management as a standard feature,
while also offering remote access to cameras and servers through proxy/direct
NAT connection. There are 2 fundamental approaches: Some traditional
VMSes offer this service with no subscription cost; cloud-native VSaaSes
typically charge recurring subscription fees, though there is at least 1 VSaaS
moving away from this model. Integration with cloud-based SAML/SSO user
management is common in 2022.
Monitor
Surveillance video is viewed by many user types (e.g. security guards, investigators,
facilities managers) for live monitoring and searching for incidents. Surveillance
video is typically displayed by software that can be run on a PC or phone, however
standalone hardware decoders can also be used.

Copyright IPVM 22
• PC/Workstation: The most common method for viewing video, a software or
web-based application authenticates users and decodes video. PC hardware
costs may increase significantly as the number of cameras viewed increases,
as some applications recommend GPU hardware accelerated decoding.
• Video Wall: Multiple screens are used to dynamically view cameras across 1 or
multiple panels. Hardware-based video walls are very expensive and required
specialized techs to install. VMS client-based video walls are less expensive
but still require expensive GPU drivers and additional setup.
• Phone: Offering fewer features than PC or Video Wall, phone clients typically
support 1-4 live simultaneous live views and basic recording search
functionality. Many phone clients do not offer analytic searching and have
limited video export features.
• Public View Monitors: Installed in locations viewable by the general public (e.g.
main entrances, POS terminals), these raise awareness of non-security users
that an area is monitored. Typically these use hardware decoders or small PCs,
and display 1-4 cameras.

Copyright IPVM 23
• 3rd Party Remote Monitoring Center: Typically these off-premise locations use
a combination of PC/Workstation monitoring and Video Walls to watch
multiple customers simultaneously. They commonly offer live guard
monitoring, video and burglar motion alarm verification, or a combination of
both.
Common Surveillance Architectures
The 3 most common surveillance architectures are SMB, Enterprise, and
ClosedCloud.
Small/Medium Business (SMB) systems typically use low-cost IP cameras with
recorder-based storage. Recorders used in these systems are also generally low-cost
and are used for management, but do not offer analytics. PCs/Workstations are used
for viewing live and recorded video:

Copyright IPVM 24
Enterprise systems generally have higher counts (100+) of IP cameras with server-
based storage and use a VMS for management. Centralized server-based analytics
are more common than camera or cloud-based analytics and monitoring consists of
PCs, video walls, mobile, and public view monitors:

Copyright IPVM 25
Closed cloud systems commonly include many features in the IP cameras at the
edge, including 15-120 days of storage. AI analytics and management are provided in
the cloud. Monitoring is typically provided by a web-based viewing client and mobile
app:

Copyright IPVM 27
Video Surveillance Cameras 101
Cameras come in many shapes, sizes and specifications. This 101 examines the basics
of cameras and features used in 2023.
In this chapter, we review the basics of cameras, including:
• Core components of cameras
• Analog vs. IP cameras
• Common components
• Form factors
• Resolution
• Video quality concerns - Day, Night, Sunlight, Shadows
• Bandwidth basics
Core Components of Cameras
Every camera has 3 core components - lens, imager, and transmitter.

Copyright IPVM 28
Lenses Control View
The lens determines how wide or narrow a view the camera will capture, referred to
Angle of View. They are identified by their focal length, which is measured in
millimeters (e.g., 2.8mm, 8mm, 12mm, etc.), with smaller focal lengths capturing
wider areas and longer focal lengths capturing narrower areas. See our Lens Focal
Length Tutorial for more details.
Lens Types
There are two main types of lenses, fixed and varifocal. Fixed lenses are set to one
field of view and cannot be changed or fine tuned to improve the camera's view.
Varifocal lenses allow users to adjust the lens so it may be zoomed in if longer
distance is required or zoomed out to capture a wide area.

Copyright IPVM 29
Varifocal/motorized lenses are much larger than fixed lenses and take up more
room, resulting in larger overall camera size, shown here side by side:

Copyright IPVM 30
Imagers Capture Light
The camera's imager is the component that actually captures the light allowed in by
the lens, also called an image sensor or simply sensor. The sensor contains a grid of
pixels that capture the intensity of light and transmit this information to the
camera's processor.
Imagers are generally referred to in fractions of an inch, measured diagonally from
corner to corner, e.g. 1/3", 1/2", etc., with most cameras today using imagers
between 1/2" and 1/3".

Copyright IPVM 31
Imager Pixel Size Differences
The size of the pixels contained on an imager varies widely, depending on the
imager's size and resolution. All things being equal, the bigger the imager, the bigger
the pixels it contains. However, if you add more pixels (e.g., going from 1080p to
5MP) and the imager size stays the same, the pixel size decreases.
Transmitter
The transmitter is the portion of the camera that sends video to the recorder or
viewer, either converting it to Ethernet in IP cameras or into a high definition analog
format (detailed below). This component could be contained directly on the camera,
or attached to the end of a length of cable (called a cable whip).
For example, the two cameras below are very similar in construction, resolution,
etc., but one outputs video via Ethernet and the other analog using a BNC connector.

Copyright IPVM 32
Analog vs IP Cameras
While analog and IP cameras have the same core components, the fundamental
distinction between the two camera types is encoding - IP cameras have video
encoding built-in, analog cameras do not.
When discussing "analog" cameras, typically we are referring to HD analog, which is
a generic term for cameras that transmit HD video (720p to 4K currently) over
standard coax cables. The main advantages of these models over IP cameras are the
ability to use existing coax cabling (making upgrades cheaper) and lower average
cost.
However, IP cameras generally offer a wider selection of form factors and features.
Audio, analytics, and I/O are rare features in HD analog models, but common in IP.
Additionally, IP cameras may be connected anywhere on the network, instead of
requiring a home run to a recorder using coax cable.
See more details in our HD Analog vs IP Guide.
Common Components
For IP cameras, which constitute the overwhelming majority of new cameras
deployed, the most fundamental way to do encoding is to use a 'system on a chip'
typically called a SoC.
A SoC is a single chip (shown below) contained in the camera which essentially
functions as its "brain", controlling image processing, compression and encoding,
network access, storage, audio, analytics, and more. All cameras use some sort of SoC,
whether IP or analog.

Copyright IPVM 33
SoCs are located inside of cameras on one of their mainboards and are normally not
exposed without doing some disassembly of the camera. Beware, this disassembly
generally voids the camera's warranty and is shown here for demonstration!

Copyright IPVM 34
That being said, if you absolutely need to confirm what chip is inside your camera(s),
disassembly may be your only option. We demonstrate this process on multiple
cameras in this report: How To See If Your Camera Uses Huawei Hisilicon Chips.
Integrated Infrared (IR)
Many cameras include built-in infrared illuminators which add light to the scene to
improve low-light images without adding visible light to the area.

Copyright IPVM 35
The upside is brighter images and better details. The downside is that only black and
white images are possible with IR and some illuminators may cause overexposure.
We discuss these issues in more detail below.
Audio
Many cameras include audio capability, which may be in several different forms,
including built-in microphones, input/output jacks, or terminals.

Copyright IPVM 36
Built-in mics are typically regarded as the simplest method of recording audio as they
do not require additional installation of external mics, but they are generally lower
performing (noisier and lower sensitivity) than external purpose-built mics.
Using microphone inputs allows users to select a purpose-built mic appropriate to the
installation, such as vandal resistant wall mount, recessed ceiling, door phone form
factor, etc.

Copyright IPVM 37
Note that while audio is frequently included in cameras, it is rarely used. Only ~10%
of video surveillance installations use audio, as shown in our latest audio stats. In the
US and elsewhere, specific requirements for audio recording must be met, and often
the party being recorded must actively consent to the recording.
We cover these issues in more detail in our Audio Surveillance Guide.
Storage Support
Lots of cameras support onboard storage using SD cards, allowing video to be stored
locally on the camera (called Edge Storage).

Copyright IPVM 38
There are two main ways of using this recording:
• Full time/no VMS/NVR: Edge storage may be used as the only storage, with
no central NVR/VMS, sometimes used in small systems.
• Redundant recording: Or it may be used for redundancy, recording to the
camera while the network is down and the recorder cannot be reached,
then offloaded when network connectivity is restored.

Copyright IPVM 39
Like audio, while many cameras include SD storage, it is not widely used. SD card
capacities have historically allowed for only a few days' recording, while hard disk
drives used in NVRs allow for weeks or months. Additionally, early adopters of edge
recording suffered from many reliability issues, which led to a lack of trust in the
technology.
However, many VSaaS providers are using edge storage as part of their end-to end
cloud-managed systems, such as Verkada and Rhombus. These manufacturers store
video on the camera and allow users to retrieve it via the cloud, instead of uploading
all video to be stored in the cloud.
Analytics
Many SoCs used in modern IP cameras include support for video analytics on board,
but the quality of these analytics has historically been fairly weak, with numerous
false alerts on shadows, lights, etc. (see our IP Camera Analytics Shootout).
However, new SoCs with AI engines built-in have begun to change this, with
improved performance in harsher conditions. Additionally, more advanced analytics
such as facial recognition and vehicle classification are now built into some SoCs. We
expect to see this trend continue as more chip options become available and
component prices decrease.
See our AI/Smart Camera Tutorial for more details on the basics of analytics.
Form Factors
Cameras are available in several different form factors, with domes, turrets, and
bullets the three most common today. Box, cube, covert, and door station cameras
are also available but are less common.

Copyright IPVM 40
Each of these form factors has its own pros and cons:
• Domes: Domes' main advantages are improved aesthetics and better
availability of outdoor/vandal options than others, but generally are limited in
longer lens options compared to boxes/bullets and must be routinely cleaned
to prevent image quality issues.
• Bullets: Bullets are next most common, with the main advantage of longer IR
range than domes (generally, not always) and more overt appearance for
greater deterrence. However, they are easier to knock out of position and
typically not IK10 vandal rated, unlike domes.
• Turrets: Turrets are a relatively new variant of dome which place the imager
and IR illuminators into a spherical gimbal. These models typically have the

Copyright IPVM 41
same advantages and disadvantages of domes, but may be more resistant to
IR glare and dirt/scratches than domes due to the smaller surface area.
• Box: Box cameras biggest advantage is flexibility, with the ability to change
lenses to cover very wide or very narrow areas, greater than dome or bullet
models. However, they are more complex to install, requiring a separate
enclosure outside, and have similar disadvantages to bullets in susceptibility
to vandalism and aesthetics.
• Cubes: Cubes are essentially mini box cameras without the ability to change
lenses, eliminating their main advantage. However, cubes still see some use in
lower cost or DIY installations.
• Covert/door station cameras: These form factors are generally used only in
the specialized applications they are intended for, such as hiding the sensor in
walls/ceilings/doorframes, or placed outside entry points. These segments
have grown, but remain niche.
For more details on these form factors and their advantages and disadvantages, see
our Camera Form Factor Guide.
Resolution
One common differentiator amongst cameras is resolution. A camera manufacturer's
specific resolution is based on the physical number of pixels in the imager, also called
pixel count (not the actual resolving power of the camera). This is most often
specified in megapixels, the number of millions of pixels (e.g., 3 megapixel, 5MP,
8MP, etc.), though some use standard HD terminologies, such as 1080p or 4K.

Copyright IPVM 42
The most common resolution in use today is 4MP, which, generally speaking, offers
the greatest balance of image quality, pixel count, and low light and WDR
performance (discussed below). From our 2022 resolution statistics:
The table below summarizes the most common resolutions used in production video
surveillance deployments today. Note that VGA is no longer common except in
thermal cameras, but is included here for reference of what 'standard definition'
refers to.

Copyright IPVM 44
Video Quality Concerns
Video quality problems most commonly emerge at night or when facing a mix of
sunlight and shadows.
At night, images may simply be dim, with subjects difficult to see at all, let alone
capturing facial details or images may be very noisy because of high levels of
processing required to produce an image. Finally, some cameras may blur subjects at
night, due to extreme digital noise removal or slow shutter speeds used to attempt to
brighten images.
The most common remedy to these issues is to use integrated IR. However, users
should beware that even IR cameras may still suffer from blurry details. And, many
illuminators overexpose subjects, washing out details of their face and clothes.

Copyright IPVM 45
Low light is not the only issue to watch out for. Scenes which contain a mix of bright
light and shadows also cause issues for many cameras, with subjects either too dark
to see or overexposed and washed out. Cameras using Wide Dynamic Range (WDR)
techniques attempt to even out the exposure across the entire scene, making details
more visible overall.

Copyright IPVM 46
Bandwidth Basics
Essentially all surveillance video is compressed because storage and network
demands would easily be 100x greater if it was left uncompressed. Compression
essentially means that the camera's SoC encodes pixels and groups of similar pixels
into a smaller format by reducing and eliminating redundant or unneeded data.
In surveillance, encoding most often uses H.264 or H.265 codecs

Copyright IPVM 47
(compression/decompression schemes). These codecs function similarly, by sending
one whole image, called an I-frame, followed by a group of partial pictures (P-
frames) that contain only areas in the camera's view which have changed.
As a simple example, a person waving would be encoded as one whole image of the
person, followed by the waving arm, as the person's body does not move.
In practice, compression is not quite this simple, but similar. For more details on how
codecs work, see our Surveillance Codec Guide.
In addition to these standard codecs, manufacturers have introduced smart codecs,
a collection of related techniques which automatically adjust compression (example
below), and other factors to reduce bandwidth.

Copyright IPVM 48
In our tests, smart codecs can reduce bitrates by as much as 95%, a huge impact on
bandwidth and storage in modern systems. Smart codecs are highly complex with
many factors outside the scope of this guide, but users should be aware of them. See
our Smart Codec Guide for more details.
Widely Used Camera Manufacturers
The two largest camera manufacturers in the world, based on the number of units
produced are Hikvision and Dahua. Both are gigantic, relative to the rest of the
market, but Hikvision is more than twice as large as Dahua. Inside China, both
companies are the most widely used high-end video surveillance camera offerings. In
the West, though, Dahua and Hikvision cameras are more frequently used in SMBs
and differentiate by selling at significantly lower prices. However, the US ban and

Copyright IPVM 49
blacklisting have significantly negatively impacted Hikvision and Dahua in the US and
globally.
The 3 most commonly used non-China surveillance camera manufacturers are
USA's Motorola (i.e., owner of Avigilon), Japan's Canon (i.e., owner of Axis), and
South Korea's Hanwha Techwin (formerly Samsung). Avigilon differentiates most
notably on analytics, Axis on breadth and quality of imaging, and Hanwha on price.
Of the 3, Hanwha comes closest in price to Dahua and Hikvision, which is part of the
reason ~55% of integrators choose Hanwha as an alternative to Hikvision and Dahua.
While the race to the bottom reduced the number of camera manufacturers in the
market (e.g., Sony exited), Bosch, Mobotix, Panasonic (now i-Pro), and a handful of
other non-China manufacturers remain.
Inside China, while Hikvision and Dahua dominate, Uniview (UNV), TVT, Tiandy,
Sunell, and a number of other manufacturers continue to compete, increasingly
benefiting from political challenges to Hikvision and Dahua.
Finally, many OEMs remain, these are companies that mostly relabel finished goods
from Asian manufacturers, most commonly Dahua (see OEM directory) and Hikvision
(see OEM directory).
Increasingly, these manufacturers are competing based on incorporating analytics
and cloud / VSaaS.

Copyright IPVM 50
VMS 101
This guide teaches the fundamentals of video management software.
We cover:
• NVR vs VMS
• Viewing Video - What are common client features? Layouts? Camera Lists?
Timeline?
• Searching Video - How is video searched for? Time? Motion? Analytics?
Thumbnails?
• Exporting Video - How is video exported? File Format? Watermarks? Cloud?
Mobile?
• Camera Discovery/Addition - How are cameras discovered/added? Auto?
Manual?

Copyright IPVM 51
• System Administration - How is the system configured? Recording? Users?
Health?
• Mapping Support - How are maps supported? Images? Hosted Maps?
Dynamic?
• VMS vs Low-Cost NVRs - What are pros and cons of each? Cost? Features?
• Installation Comparison - Why are NVRs easier to install? Training? Skills?
• VMS Hardware Flexibility - How are VMSes more flexible and scalable? Open?
• Widely Used VMSes/NVRs - What companies are widely used or notable?
NVRs vs VMS
The fundamental difference between Video Management Software and NVRs (or
Network Video Recorders) is that NVRs preload and bundle the video management
software in their appliances with an operating system. As such, VMSes require
hardware (e.g. on-premise physical, virtual machines, or in the cloud) and an
operating system to function.

Copyright IPVM 52
Secondarily, VMS software tends to offer greater functionalities than NVRs, though
this is a rule of thumb, not required. Indeed, many VMS software providers offer their
own NVR options for simplicity of deployment. Also, many NVR providers offer a thick
client that supports connecting to multiple NVRs simultaneously with increased
functionality and complexity.
Viewing Video
The most common function of any surveillance system is viewing live and recorded
video.

Copyright IPVM 53
Surveillance video is viewed on software typically referred to as a "client", which
displays the video from cameras or recorders on screen. There are typically VMS
clients that run on PC/workstations, web browsers, and smartphones.
Primary client interface features consist of viewing tiles, camera lists, control
function menus, video timeline, instant replay features.
Viewing Tile Layouts
Viewing multiple cameras at the same time is a common live monitoring task.
Viewing Tiles/Layouts allow for organizing views of multiple cameras (2x2, 3x3, 4x4)
for quick access to what camera views are important or physically and operationally
related to each other (e.g. all entrances, all parking lots, cafeteria cameras):
However, watching a lot of cameras simultaneously is difficult, and visible details can
be lost as each camera is very small.
Camera Lists
Camera Lists allows for quick access to cameras for selection, however, in a larger
VMS lists can be difficult to visually search. Some VMSes offer organizing cameras

Copyright IPVM 54
alphanumerically and/or by building name/location, or a quick search bar for easier
access:
Common Control Function Menu
Control Function Menus offer quick access to common actions like Taking Snapshots,
PTZ controls and Creating Bookmarks.
• Snapshots: Because sometimes a still image is what is needed, rather than a
video clip, snapshot tools capture and save an image, which is smaller and
easier to share than video. However, snapshots may not represent the entire
incident.

Copyright IPVM 55
PTZ Controls: Moving PTZ cameras manually to a target is fundamental for high-
security live monitoring. Proper permissions and camera position settings
should be made to ensure the camera is not misused (e.g. looking directly at a
wall, moved by a second user while monitoring an incident,
etc.).
Bookmarks: When investigating video, bookmarking allows for easier sharing of
video by tagging a video clip in the VMS which allows searching for the
bookmark instead of by time or motion. However, creating bookmarks is
fundamental for them to be available for searching.
Video Timeline
Video Timeline allows a user to quickly scrub through a lot of video ( manually
adjusting the current time of being viewed):
However, Video Search tasks are better designed for looking for specific events or a
previous day's footage.
Some VMSes support switching between live and playback video by using the
Timeline.

Copyright IPVM 56
Instant Replay
Security operators can miss important details while monitoring live video, and
Instant Replay offers short playback times (15s - 5 mins) while viewing live video,
saving the time to open a dedicated video search.
The time limit for rewinding means the incident needed to be viewed live for instant
replay to be useful.
Searching Video
Searching for recorded video is a critical feature for a VMS, and offering a fast means
to search for a person/vehicle, event, or left-behind object is important for most
users.

Copyright IPVM 57
There are different search types common in many VMSes, each with pros and
cons, depending on what information is known. Time, motion, analytics,
thumbnail and bookmark searches are the most common types
Time-based Search
Time-based search is the most basic search and is useful if the system does not offer
analytics and only a time range is known
for an incident:
Time-based searches can take significantly
longer than most other search types
because you have to scrub through the full
time range of video.
Motion Search
Motion search is generally quicker than time-based search because it shows only
motion events on a camera which can decrease the time needed to find a recorded
incident.

Copyright IPVM 58
However, if motion was not detected by the camera, or if there are many motion
events on a camera, motion search can take as much time to review as time-based
searches
Analytic Search
Because time and motion searches can take a long time to go through results to find
people, vehicles, or objects, analytic searches can greatly increase search speed:
However, classification errors (person vs vehicle, male vs female) can cause poor
results. Moreover, in 2023, because it is atypical for VMSes to offer robust analytics
capabilities, analytics search offerings will vary widely, depending on the VMS.

Copyright IPVM 59
Thumbnail Search
Thumbnail searches can be quicker than time or motion searches when an object
(vehicle, bag) enters or leaves the view of the camera, and analytics are not offered
by the VMS/cameras.
However like time-based searches, video still must be manually searched, and quick
events may be missed between 2 thumbnail images.
Bookmark Search
Bookmark search offers quick searching for an event that have been previously
identified events:

Copyright IPVM 60
Exporting Video
When crimes, accidents or problems occur, exporting video from one's video
surveillance system is critical to proving incidents.
There are features common to many VMSes, including export time selection,
proprietary video formats, multi-camera export, exported file players,
watermarking/encryption support, open file formats support.
Export Time Selection
Setting the correct start and stop time quickly and allowing users to make final
adjustments before exporting video, without having to restart the export process
makes a VMS easier to use:
However, additional options in the Export menu can cause confusion for nonregular
users.

Copyright IPVM 61
Proprietary Video Formats
Exporting video in the VMS's proprietary format is quickest and does not require
extra processing/time compared to converting to open-formats. Also, analytics and
events are included with proprietary formats, which can be valuable for review:
However, proprietary video can only be reviewed in the VMS client or standalone file
player, which can be difficult for 3rd parties (e.g. Police departments, lawyers).

Copyright IPVM 62
Multi-Camera Export
Rather than having to export video from each camera individually, multi-camera
export can save significant time by allowing users to export multiple video files from
different cameras simultaneously:
While this is more efficient than single-camera export, it can add complexity when
reviewing and file management.
Export File Player
Because proprietary video formats require special software for playback, export file
players offer playback without installation of a VMS client and VMS-lite features (e.g.
zoom, snapshots, fisheye dewarp, events and bookmarks):

Copyright IPVM 63
These add complexity compared to playing back in open-format players (e.g.
Windows Media Player, VLC, QuickTime).
Watermarking / Encryption
Because some end users have to ensure that exported video is not tampered with,
watermarking and encryption can be added to the video to ensure it has not been
edited or tampered with:
This takes additional time and processing
to watermark and/or encrypt video when
exporting and when playing back.

Copyright IPVM 64
Open File Formats
Exporting to open-format video files (e.g. MP4, AVI) means that users can more
easily view exported video because they can use common video players (e.g.
Windows Media Player, QuickTime, etc.):
Advanced features like snapshot, digital zoom, and events/bookmarks are lost when
converted to open formats
Camera Setup, Configuration, and Maintenance
Camera setup, configuration, and maintenance are the most common tasks when
managing a surveillance system.
There are camera management features common to many VMSes, including
discovering and adding cameras, adjusting camera settings, camera health

Copyright IPVM 65
monitoring, firmware management, camera audit reports, and camera
replacement tools.
Discovering and Adding Cameras
Camera discovery and adding cameras to a VMS are the first steps to building a
system, and should be a quick and semi-automatic process that scans for any
cameras online and waiting for a connection:
However, because automatic discovery may not always work depending on the
network, manual search and adding tools can be used.

Copyright IPVM 66
Adjusting Camera Settings
Ensuring correct camera settings (resolution, frame rate, compression) are critical to
the proper function of a VMS, based on the system configuration for bandwidth and
storage requirements:
However, not all VMSes support adjusting camera settings for all camera
manufacturers.
Camera Health Monitoring
Because a VMS is only effective when cameras are connected and recording,
camera health monitoring tools perform a critical function. VMSes typically send out
alert messages if a camera is offline or not recording, additionally, some offer
online/offline health status reports that can be monitored live:

Copyright IPVM 67
Large VMSes can be difficult to monitor camera health live, while inaccurate false-
positive camera offline alerts can lead to support staff ignoring them.
Firmware Management
Updating camera firmware is important because helps keep a VMS cyber-secure and
as smart cameras become more common, it ensures that analytics are up-to date
and accurate as possible:

Copyright IPVM 68
Updating firmware through a VMS is typically limited to same-manufacturer
cameras, and often requires using a standalone Camera Configuration Manager from
the camera manufacturer.
Camera Replacement Tools
When cameras fail or are updated, being able to easily copy the configuration of
settings from the previously installed camera to the new camera is a helpful tool for
service technicians/installers:
However, camera replacement is only common in enterprise-focused VMSes.
System Administration
There are many variables beyond the camera configuration when setting up a
VMS.
While each VMS offers methods of configuring and rules/limitations for the
configuration that will vary significantly depending on the VMS and licensing, there

Copyright IPVM 69
are common categories including storage, recording, scheduling, and user
permissions.
Storage
Where video is stored and how long it is stored is one of the primary decisions when
setting up a VMS. Setting video to recording in the correct server drives, and what
percentage of the drive to record to will impact how the VMS will operate.
Because some customers have minimum required days of recording, and others
maximum allowed days, proper configuration of how long to record video is a
necessity.
Many VMSes support per-camera retention settings, however, this can add
complexity to system administration.
Recording
Because of the differing needs of customers, selecting the type of recording (e.g.
motion, continuous) is important to provide the expected recorded video.

Copyright IPVM 70
This can significantly impact the amount of storage required. Moreover, different
camera settings can be applied for live monitoring and recording for increased
efficiency.
Scheduling
Because there can be different priorities for recording depending on the time of day
or week, schedules are primarily used for storage optimization. This is less commonly
used in 2023 as storage costs have decreased significantly. They can also be used for
controlling alarm notifications and user login:
Depending on the complexity of the configuration this can require significant
additional setup time of the VMS.

Copyright IPVM 71
Users - Permissions and Groups
Controlling what cameras users are allowed to see, and when/how they access the
system is important. VMSes offer user and user group configuration:
Individual logins (user account per person) provide the highest level of security and
user auditing, however, even in 2023, shared logins for monitoring staff, who may
share a monitoring PC is still common.
Many VMSes support creating user groups to minimize creating individual
permissions for each user, however, this is more common in high user count VMSes.
Mapping Support
Understanding where incidents are happening while live monitoring and
understanding the physical relationships between cameras on a VMS is important
for security operators, but can be difficult as the number of cameras increases.

Copyright IPVM 72
There are common map support features including 3 map types, map interaction,
and map alerts.
Map Types
There are 3 primary map types:
• Static Images (2D): Easiest to use and configure, but limited in the features
and visual feedback offered in multi-dimensional and Live mapping. Multi-
dimensional (3D): It can be easier to track suspects between camera views
because it provides stronger visual cues and camera view orientation than
static 2D mapping. However, it can be more difficult to configure and can be
confusing for non-regular users.
• Live Geospatial (GIS): Offers a higher level of integration to systems like
vehicle location and dispatch commonly found in public safety and high
security operations. It is typically an expensive addon to standard VMS
mapping.
Map Interaction
Interactive maps offer better visual cues for VMS events for live monitoring, which
allows security users to more easily track suspects or identify suspicious behavior.
Additionally, with larger high-security systems (e.g. Jails, Airports, Casinos), access
control, intrusion, LPR and other systems are commonly integrated into maps for
unified system monitoring.

Copyright IPVM 73
However, very dense interactive maps can create confusion for operators, which will
negatively impact live monitoring.
Map Alerts
Visible notifications for alerts and alarms can notify operators when there is an
incident, while also showing them where on the map the alert was triggered,
decreasing their response time.

Copyright IPVM 74
However, large maps with many alerts can be more visually distracting and confusing
than a basic scrolling alert/alarm list.
Rising Availability of Low-Cost NVRs
A major development in the past 5 years has been the increase in the availability of
very low-cost Asian appliances, far less expensive than their Western counterparts.
Western VMS-based appliances are generally 3-5 times more expensive than Asian
produced ones. 8 - 32 Channel NVR Appliances from Asia ranges in price from $200
USD to under $1,000 USD, in comparison to Western VMS-based appliances in the
$2,000 - $10,000 USD range when fully licensed.
The potential advantages to Western appliances include:
• More native camera driver integrations
• More developed user interfaces with advanced Video Search and Export
functionality

Copyright IPVM 75
• Active Directory support
• Multi-Server/NVR management
• System Health Monitoring
• User Auditing
• GIS Mapping
• Edge Recording
This chart compares the primary pros/cons:
Appliances Quicker to Setup
Since the software is pre-loaded on appliances, the installing technician avoids
installing the Operating System, the recorder application itself, database
management software, etc. This can save a few hours and eliminate any errors from
installing manually.

Copyright IPVM 76
Appliances Easier to Deploy
Since appliances can generally start connecting to cameras right after turning them
on, it eliminates the need to know how to set up a PC/server and install a
recorder/VMS application. For some organizations, this level of knowledge is trivial
and poses no advantage, however, for others, this may allow them to do
deployments with less skilled technicians.
VMS for Server Manufacturer Flexibility
A disadvantage with appliances, you have to use the manufacturer's hardware.
Two common problems occur here:
• Your organization has standardized using a certain provider (e.g., Dell, HP,
etc.) and you have preferred terms and servicing policies with that
organization. An appliance bought from a different provider falls outside
that process and may be against the rules or cause logistical issues.
• You prefer certain hardware specifications or component choices and are
not comfortable with selecting the appliance provider. This is generally
only an issue for advanced technical users.
In either case, the ability to pick your own hardware is one of the advantages of
using a Software Only solution.
Virtualization with VMS
Because Software Only VMS can be installed on any Windows/Linux server platform,
many are certified for use in a virtual server environment.

Copyright IPVM 77
If the hardware infrastructure exists, this will save hardware cost but can add
complexity and coordination for installation and support. Installing on a virtual
server can require a higher level trained, higher cost technician.
Appliances Simplify Troubleshooting
Since the manufacturer selected the hardware and pre-loaded the software, they
have much more control and a better understanding of what might go wrong and
how to troubleshoot it. It is much harder for the manufacturer to troubleshoot rd
software loaded on 3 party hardware as there are many more variables.
It might simply result in more time and more issues but, in the worst case, it can
result in finger-pointing and blame being passed that it is your hardware's issues.
One common concern with purchasing hardware and software separately is that it
can result in having two organizations responsible for troubleshooting the
surveillance system. The PC/server hardware may fall into one group while the
software/application falls into another. This can increase complexity and cause
delays in resolving problems compared to an appliance.
Many Appliances Require No Annual Software Costs
Many, but not all software-only providers, charge annual costs for
maintaining/upgrading their software. These costs can double the upfront license
cost over a 5 year period (e.g., pay $150 for a single channel license plus another
$100-$150 over the next few years). By contrast, appliance providers typically do not
charge annual fees and, often, give away software upgrades.

Copyright IPVM 78
VMS providers often argue that those annual fees result in much more advanced
functionality than appliances do. Sometimes this is true but not always. This point
should be carefully considered on the exact platforms one is considering.
VMS Provides More Flexibility to Scale
Appliances typically only are offered in a handful of configurations and often have
restrictions on how they can be expanded.
As a general rule, if you exceed the 16/32 channel limit by even 1 or 2 cameras, you
will need to add an additional appliance. By contrast, by using a software only
solution, you can switch or add components or machines as you please, as
VMS systems generally support a much higher camera per server count (128500).
Of course, this assumes you have the technical skills to do so but for those that do;
this can make it easier to scale the surveillance system with more cameras, higher
resolution, longer storage, etc.
Many appliance vendors now offer external NAS/SAN storage options to expand
overall storage capacity, but those do not add more throughput capacity, just longer
retention times.
Appliances Easier for Small System Deployments
The labor cost for setting up VMS software is fairly constant, whether the machine is
handling 4 or 64 cameras. It might take 3 hours to set up a machine recording 4
cameras and perhaps 5 for one recording 64 cameras.
However, the cost of system initialization as a percentage of the total job is much
higher in the 4 camera deployment. As such, appliances are particularly attractive for

Copyright IPVM 79
sites with small cameras. For instance, even if a user had 1000 cameras but they were
split across 250 locations, appliances would provide a significant advantage as it
would eliminate the labor of initializing 250 machines.
VMS More Common in Large Systems
Most appliances are targeted at small to midsize camera counts - 4, 9, 16, and 32
cameras being the most common. If you have more cameras at a given site or more
demanding requirements (super high resolution and/or frame rates), generally
software only is a better option as you can customize the hardware for your more
demanding needs.
Widely Used or Notable VMSes and NVRs
Below is an alphabetical list of widely used or notable VMS and NVR manufacturers:
• Avigilon: Focuses on a tightly integrated VMS / IP camera/video analytics
offering, and differentiates on the VMS side via their own search and real-time
analytics (see tests, 1, 2, 3).
• Axis: The first IP camera manufacturer and still the largest non-China one, Axis
offers a variety of VMSes but they are generally less commonly used and
marketed.
• Dahua: A major China camera manufacturer, they also offer NVRs that are
widely used due to low-cost but not considered high quality.
• Exacq: A long-standing VMS, once one of integrator's favorites, has declined
in favorability since Tyco acquired them in 2013.
• Genetec: One of the earliest VMSes and still independent, Genetec has
evolved to be a very high-end provider, whose pricing is uncompetitive in

Copyright IPVM 80
most of the market but whose advanced functionalities are frequently chosen
by enterprise end-users.
• Hanwha: A major and rising camera manufacturer, they offer their own NVR
and OEM Network Optix's VMS.
• Hikvision: The world's largest video surveillance manufacturer, in the West,
they are primarily a low-cost camera and NVR manufacturer. Their pricing
makes them widely used but their NVRs are viewed unfavorably by
integrator's overall.
• Milestone: One of the earliest VMSes, Milestone offers a broad range of
options, from free to enterprise, they focus on being 'open' in terms of 3rd
party support and community. They have declined in favorability in the past
few years and have split their cloud offering with their sibling company
Arcules.
• Network Optix: A relatively new VMS (founded 2011), they primarily OEM
their software to partners (Hanwha, Digital Watchdog). They have risen in
favorability as a lower-cost mid-market alternative to Exacq and Milestone.
• Uniview (aka UNV): The 3rd largest China video surveillance manufacturer,
they are gaining in both camera and NVR use in the West, due to bans for
Hikua, their NVRs have similar problems to Dahua and Hikvision.
See also our Directory Of 120 Video Management Software (VMS) Suppliers for a
more complete listing of VMS manufacturers.

Copyright IPVM 81
Video Analytics 101
This guide teaches the fundamentals of video surveillance analytics.
We cover:
• Why Use Video Analytics
• Video Analytics Warning
• Where are analytics performed? Camera? Server? Cloud?
• Original VMD-Based Analytics
• Why AI Analytics? Detection and Classification
• Basics of AI Analytics
• Types of Objects Types of Events
• Color Analytics
• AI Offers Advanced Detection
• AI Classification Issues
• Deep Learning Is Not Ongoing In Cameras

Copyright IPVM 82
• Hardware Limitations on Cameras
• GPU Hardware Expensive on Servers and Recorders
• Deep Learning / AI Mainstream in 2023
• Single Vendor vs. Open Systems
• Video Analytic Provider Overview
Why Use Video Analytics
Historically, surveillance relied on a person to monitor multiple cameras
simultaneously and visually analyze what was occurring in each in order to detect
events. However, this is inefficient and prone to errors because humans can only
monitor so much video at a time, leading to missed events.
Video analytics analyze camera streams to detect specific events, such as people or
vehicles moving, visible faces, or license plates. These events can then be used to
trigger recording or notify an operator so they may effectively monitor more
cameras at one time.
Video Analytics Warning
Before moving on with specific details, we want to make one warning clear:
Video analytics performance varies drastically, ranging from very strong to
terrible. Users should not trust manufacturer marketing in analytics, as it has
historically made many unsubstantiated claims and overstated performance.
There are high-performing analytics available, with more new entrants and
advancements in the past few years which will further drive performance, but these
are exceptions, not the rule.

Copyright IPVM 83
Historic Method: VMD (Video Motion Detection)
Video motion detection (VMD) is the original means used by cameras to try to detect
activity. It is not AI nor 'smart' but some may market it as such.
VMD depends on analyzing rather simplistic changes to pixels from one frame to the
next. If enough pixels change by enough of a level, the camera triggers motion
detected.
The image below shows how VMD might 'see' a car moving and correctly detection
motion:
VMD Prone To False Alerts
However, VMD is not intelligent enough to know whether pixels are valid movement
or not, only that they are moving. Because of this, it makes many mistakes and things
like shadows, leaves, branches, animals, and others may trigger it. For example, the
image below shows VMD making a mistake because it does not 'know' whether the
changing pixels are a person or a shadow:

Copyright IPVM 84
Basic Analytics Adds Filters
To try to reduce false alerts, manufacturers developed filters that attempted to
remove false motion events, leading to basic video analytics. These filters may
include things like height/width ratios, object speeds, repetitive motion, etc. They
improved performance compared to simple VMD, but false alerts were/are still very
common, with shadows, lights, rain, animals, and other things triggering many
analytics (see our Camera Analytics Shootout).
Why AI Analytics
Because of the limitations to original VMD-based analytics and its many false alerts
per day, AI analytics were developed for video surveillance to increase detection
accuracy and offer significantly more classification capabilities.
The most fundamental advantage of AI analytics is the system is actually effectively
asking for each detected object: 'Is this a person?', 'Is this a car?', 'Is this a bicycle?',
'Is this a gun?' etc.

Copyright IPVM 85
Basics Of AI Analytics
AI analytics achieve more accurate performance by applying 'training' to their
algorithms. Simply put, large numbers of valid objects are analyzed by these
algorithms, which decides which features determine whether the object is what it is
labeled as.
To have a system learn to detect faces, many face images are fed into the AI system
so it can analyze and learn what a face looks like:
This training is performed automatically, without an engineer defining all of these
aspects in advance. The algorithm 'learns' on its own, referred to as deep learning.
AI Object Detection
The most basic level of AI is determining if the camera is looking at a real object or
noise (wind, dust, shadow, headlight). While more accurate than simple VMD, AI is
still challenged by environmental effects (rain, snow) and lighting changes:

Copyright IPVM 86
The next level of AI is determining if the detected object is a person or not. Detecting
specific objects (people/vehicle/animals) is more challenging than simple detection,
and requires higher-level details.
Even more challenging is determining if the object is a person, vehicle, or other
object or not.

Copyright IPVM 87
However, people and vehicles are the most common while other objects tend to only
be available in more advanced and expensive systems.
AI Object Classification
Video analytics can be trained to detect specific types of objects:
• Person - Age, gender, ethnicity, clothing color
• Vehicles - size, type (car vs truck), color, direction of travel
• Animals - type (cat vs dog), color
• Inanimate objects (bags, guns) - size, status (left-behind), type (pistol vs
rifle)
Person Classification
Because a person's physical attributes are valuable for finding a specific person
during or after an incident, AI is trained to identify gender, age, clothing type and
color, glasses, facial hair, etc.:

Copyright IPVM 88
However, most of these classifications are extremely challenging, and in 2022
outside of clothing color, most person classification analytics are not highly accurate.
Vehicle Classification
A vehicle's physical attributes are the most common sources of known information
about an incident, or when looking for a person. AI can be trained to identify the
vehicle type, color, make, model and direction of travel:

Copyright IPVM 89
However, in 2023, make and model are extremely challenging, rare, and primarily
marketed by specialized LPR/ANPR manufacturers.
Animal Classification
Much less common than person or vehicle analytics, AI can be trained to identify the
type (dog, cat), and color.

Copyright IPVM 90
Inanimate Object Classification
Detecting suspicious objects can help decrease response time to potentially
dangerous situations. AI can be trained to identify backpacks, boxes, briefcases,
guns, and classify specific characteristics of each:
However, in 2023, object detection and classification for guns are primarily offered
by specialized companies, which can be expensive and come with significant
challenges. Classification of backpacks and other inanimate objects (briefcases,
packages, etc.) can be very challenging as well, based on lighting, angles, and
distance.

Copyright IPVM 91
Color Analytics
Color can be an important detail for identifying an object when searching for a
specific person or vehicle. Color analytics can be offered by themselves or in
conjunction with other objects (clothing, vehicle color):
However, color analytics can perform inaccurately in low light or B&W IR night video,
returning colors as shades of black and gray:

Copyright IPVM 92
Events
Analytics can be trained to detect specific types of behaviors. The most common
behavior detections are:
• Tripwire / Line Crossing and Intrusion
• Loitering
• People Counting
• Object Left Behind / Removed
Tripwire / Line Crossing and Intrusion
Detecting when a person enters or approaches a restricted area can be critical in
high-security locations, or for health and safety concerns.

Copyright IPVM 93
This is the most basic behavior analytic and is offered by many cameras:
Intrusion is used for the same reasons as a tripwire analytic, but the alert is triggered
by a person entering an area, rather than just crossing a line.
Loitering
Because a person entering or approaching an area is not always enough to trigger an
alarm, if they are still detected many minutes beyond an acceptable limit, loitering
alerts are offered.

Copyright IPVM 94
However detecting loitering accurately is much more challenging than tripwire, since
the analytic cannot lose sight or detection of the person for the defined time
limit.
People Counting
Understanding the number of people who
enter and leave an area through a door can
be important to security operations for
safety concerns, and valuable for operators
of retail and hospitality companies.
People counting analytics will increment or decrement as people pass through a
defined line or area:
This is similar to tripwire analytics but does not trigger an alert for every person
detected.
Object Left Behind / Taken
Object left behind and taken analytics were developed because unattended objects
(bags, boxes, backpacks) that are left behind can be dangerous or cause confusion
and expensive precautionary site closures at high-security locations
(airports, schools, stadiums):

Copyright IPVM 95
However, because object-left-behind and taken analytics are very challenging to
perform accurately, you need to be careful to ensure your scene, and object sizes
will work with the analytic.
Beware: Some AI Specifics Inaccurate
Detecting these more specific object types also allows for the classification of human
subjects' gender, age, clothing style, etc. However, in our testing, even the best
performing analytics often incorrectly classify this demographic information, with men
classified as women, children classified as adults, etc.

Copyright IPVM 96
Where Analytics Are Performed
Analytics can be performed by cameras, recorders, servers, or the cloud.
The tradeoffs of these 4 locations are:
Camera: Camera-based analytics can be more accurate because they can be
performed on low compression, high-quality video, before encoding. However,
powerful processors required for accurate analytics have typically been limited
to expensive cameras and/or specialized capabilities (e.g.
LPR/ANPR).
Recorder: Analyzing video on a recorder generally allows for a more powerful
processor than a single camera could offer, which is used for all cameras
connected to the recorder. Also, the analytics are immediately integrated with
the viewing software. However, recorder manufacturers do not want to over-

Copyright IPVM 97
specify the hardware to keep the recorder's price low, and often the analytics
are limited.
Server: Dedicated analytics appliances are built to have increased analytics
processing than Recorders, and typically include higher-spec components.
However, server-based analytics add significant costs and can be noisy and
power-intensive.
Cloud: Analyzing video in the Cloud should eliminate the resource limitations of
recorders or servers, and provide high accuracy analytics.
However, cloud processing, specifically public cloud hosting (Amazon, Google),
can be very expensive. Cloud-based analytics services typically cost between
$25-$50 per camera per month. Moreover, Internet upload use will increase and
can limit the number of cameras that can be analyzed.
Deep Learning Is Not Ongoing
One common misconception is that deep learning continues to learn after
installation.
Most analytics use a pre-trained model that does not change their analysis over time
based on the scene. However, a small number of cameras and systems, such as
Avigilon's self-learning H5A models, attempt to learn the scene they are placed in
over a period of time.
Hardware Limitations on Cameras
The biggest limitation of doing AI inside cameras has been the cost of the hardware
to run it inside the camera. GPUs used in AI cameras have historically been very

Copyright IPVM 98
expensive. Because of that, cameras used VMD methods, knowing the deficiencies
but realizing the tradeoff in much higher cost was not viable.
However, recently prices have begun decreasing and performance increasing
significantly, leading to a slew of low-cost models claiming 'AI', with varying results.
GPU Hardware Expensive on Servers and Recorders
Because AI analytics performed in Servers and Recorders are more complex than
camera-based analytics and have to process multiple cameras simultaneously, many
require 1 or more GPU cards that can cost thousands of dollars per card.
These high costs mean that AI analytics are generally out of the reach of many
deployments, making them most likely a fit in high-end commercial, municipal, or
government projects.
Deep Learning / AI Mainstream
While video surveillance analytics has been promoted, hyped, and lamented for
nearly 20 years, in 2020, it finally went mainstream, meaning that almost all
manufacturers offer fairly accurate and reliable people detection analytics. And the
ones that do not stand out as outliers, the other way. This is the plus. See IPVM
Camera Analytics Shootout 2022 for test results from 18 camera manufacturers.
Single Vendor vs. Open System Integration
Because analytics can be difficult to integrate across 3rd parties, many vendors focus
or promote their one 'end to end' video analytics / VMS solutions. The most common
example of this is Avigilon, whose camera and video analytics are tightly integrated
with their Control Center VMS. Other notable examples include LPR analytic camera

Copyright IPVM 99
manufacturers (e.g., Genetec's AutoVu) which generally only work with their own
VMS.
Other manufacturers, such as Dahua, Hikvision, and Uniview, also better integrate
their analytics to their own recorders (including events, bounding boxes, and
configuration) while third-party support is limited to a few platforms and events only.
Video Analytic Provider Overview
Below is a list of notable video analytic providers.
Camera Manufacturers:
• Avigilon: Analytics has been a core focus of the company for a number of
years.
• Dahua and Hikvision: Heavy emphasis on adding AI to differentiate against
other low-cost providers.
• Axis and Hanwha: Both have been relatively late to release analytics, and
are just starting to bring offerings to market.
• Bosch: Has offered solid performing analytics for a number of years, but
typically in their most expensive models. They have offered less accurate
analytics in lower-cost cameras, but currently offer similarly performing
analytics.
Software-based:
• Briefcam - Most common specialist provider but is expensive, limited to
larger systems (100 camera minimum).

Copyright IPVM 100
Startups:
There are dozens of new AI analytics companies all over the world, mostly offering
analytics as add-ons via cloud-hosted servers. These are generally much higher priced
analytics than camera manufacturers, and more often offer specialized detections
(e.g. guns, weapons, fighting, etc.).

Copyright IPVM 101
Facial Recognition 101
Facial recognition interest, use and fear is increasing. This guide aims to teach you
the fundamentals of facial recognition.
We cover:
• Face Detection
• Face Recognition
• 1:1 vs 1:N
• Cooperative vs Uncooperative
• Resolution / Image Requirements
• Logistical Issues Setting Up Facial Recognition
• Avoiding / Undermining Facial Recognition - Masks, Hats, Sunglasses, etc.
• Looking Down to Avoid Face Recognition
• Crowds / High-Density Performance Issues

Copyright IPVM 102
• Liveness Detection
• Accuracy
• Ethical Concerns
• Face Recognition Providers
Facial Detection
Before face recognition can be attempted, a face needs to be detected. Typically, a
rectangular border is drawn over the face. This is a relatively common analytic, and
much easier for high accuracy than face recognition.
Performance varies on 3 fundamental metrics:
• Angle of Faces: While it is 'easy' to detect a face looking directly at the camera,
performance can vary significantly depending on how a person tilts their head
(down, left, right, etc.)
• Lighting of Faces - While it is 'easy' to detect a face looking directly at the
camera in a well-lit scene, performance will vary significantly depending on
the lighting conditions of the scene (shadows, darkness, noise, etc.).
• Computing load to detect faces - Finding and determining what objects are in
a scene and whether those objects are a face (instead of a tree, a car, a cat, a
bowling ball, etc.) can be very challenging while many video surveillance
devices (e.g., IP cameras, NVRs) have limited processing power.
A well-lit face looking directly at the camera will be easy to detect:

Copyright IPVM 103
While the side of a face in low lighting will be more challenging:
Facial Recognition
Facial recognition answers the question "Whose face is that?", which is important
information when investigating a crime or pursuing a suspicious person.

Copyright IPVM 104
Facial recognition cannot happen unless the face has already been detected.
1:1 vs 1:N
Facial recognition either verifies a person to a specific face (1:1) or identifies a
person from many faces (1:N).
1:1 matching is called Verification. A single reference photo is presented to the face
recognition system, and it determines if the face detected matches that photo. 1:1
matching is relatively easy:

Copyright IPVM 105
1:N matching is called Identification. Many photos are presented to the face
recognition system, and it determines if the face matches any of those photos. 1:N
matching is relatively hard, much harder than 1:1:
Video surveillance face recognition applications are typically 1:N for identification.

Copyright IPVM 106
Cooperative vs Uncooperative Face Recognition
Cooperative face recognition is relatively easy. This is when a person stops and
actively looks directly at the camera (e.g.,
access control, security checkpoints,
FaceID):
Uncooperative face recognition is
relatively hard because the person may be
oblivious to the camera is there or worse
may know and is trying to avoid it:
Moreover, where the camera is installed and lighting conditions can increase the
challenge, resulting in significantly lower accuracy.

Copyright IPVM 107
Logistical Issues Uncooperative Face Recognition
Because most people are unaware that face recognition systems exist and
surveillance cameras are typically installed high above the ground to cover wide
areas, uncooperative face recognition is a significant percentage of real-world
applications.
This means that achieving good face recognition accuracy with most surveillance
cameras is very challenging.
Resolution / Image Requirements
Face recognition requires significantly higher resolution or pixels per foot/meter
than typical person detection or face detection. Many manufacturers specify
requirements in "pixels between the eyes" or "pixels per face". While the
specifications vary, most manufacturers require around 100 Pixels-per-foot (~300
Pixels-per-meter).
For typical wide-angle surveillance cameras, this generally means short distances for
highly accurate facial recognition:

Copyright IPVM 108
Mounting Height / Camera Downtilt
Many surveillance cameras are installed on ceilings or above 10' the ground so they
are out of reach. This means that many cameras are aimed above or at the top of a
person's head, which makes face recognition challenging for a person or analytics:

Copyright IPVM 109
With the goal of capturing full-face images, many facial recognition vendors will
specify mounting requirements with minimum heights or maximum angles of face
capture.
Angle of Incidence / Camera Sidetilt
Because many surveillance cameras are installed on or next to walls, so they can
cover as much area of a room as possible, they do not typically provide a direct view
of faces, often capturing the side of a person's head:

Copyright IPVM 110
While most facial recognition systems will fail to match faces at a high angle of
incidence, even ones that provide a match will do so with significantly lower
accuracy or confidence compared to full-face images.
Lighting Issues
While many surveillance cameras are able to provide enough details for person
detection in low light, facial recognition requires significantly higher details and
image clarity.

Copyright IPVM 111
Slow shutter and other low-light camera techniques make facial recognition in low
light very challenging/improbable:
Many cameras include IR LEDs to illuminate dark areas and increase the details
captured by the camera. However, IR performance varies significantly which can
decrease details and decrease face recognition confidence:

Copyright IPVM 112
Avoiding / Undermining Facial Recognition - Masks, Hats, Sunglasses, etc.
Many reasons exist for people to actively or inadvertently undermine the
performance of facial recognition systems:
• Hats and Sunglasses
• Masks
• Looking Down / Away
• Crowds

Copyright IPVM 113
Hats and Sunglasses
Depending on the location (interior vs exterior) and region, hats and sunglasses are
commonly worn without reasonably raising suspicions. However, hats and sunglasses
negatively impact facial analytics confidence:
While many facial recognition systems market their ability to perform facial
recognition with hats, sunglasses, and even masks, at best, it always reduces the
confidence level of matches and therefore increases the probability of errors.

Copyright IPVM 114
Masks
Masks were historically limited to medical facilities, but have become common in
public places over the last few years due to Covid. Medical masks significantly
decrease match confidence, with people generally not recognized at all:
The decrease in face recognition confidence also does not account for the decrease
in faces that are detected while wearing masks, which would further compound the
challenge.
Looking Down to Avoid Face Recognition
Looking down is a simple but significant way to undermine facial recognition
systems. In addition to the challenge of typical camera installation heights, many
people look down, by habit (e.g. looking at smartphones):

Copyright IPVM 115
And, sophisticated criminals or other people who want to avoid detection, have
known for years that looking down makes it harder for humans or facial recognition
systems to identify them.

Copyright IPVM 116
Crowds / High-Density Performance Issues
Many locations that face recognition is marketed for (e.g. airports, schools,
stadiums) often experience large crowds of people that cause issues with most face
detection and face recognition systems.
In addition to the high number of faces causing problems, faces being
blocked/obstructed and then reappearing can cause multiple detections for the
same person in a short time period.
Liveness Detection
Facial recognition is being widely promoted as a solution to physical access control.
However, many systems do not offer liveness detection, which means they can be
tricked to believe a photo of a person is a real person:

Copyright IPVM 117
Liveness detection is less commonly offered and can be expensive or include
specialized hardware. For more, see: Facial Recognition Systems Fail Simple Liveness
Detection Test.
Accuracy For Facial Recognition
Measuring accuracy for facial recognition systems is very hard. Vendors tend to
market with simplistic, impressive-sounding metrics like 98.6% accurate:

Copyright IPVM 118
However, scientists measure facial recognition much differently:

Copyright IPVM 119
Using such curves is beyond the scope of a 101 presentation, however, it is
important to understand that these curves express the fundamental tradeoff
between false positives (i.e., matching the wrong face to someone else) and false
negatives (failing to match a face to a person on the watchlist).
NIST is a U.S Government standards
and technology department that
tests face recognition algorithms as part of their ongoing Face Recognition Vendor
Testing (FRVT).

Copyright IPVM 120
Strong NIST results are cited by many manufacturers as a marketing tool, however, it
can be difficult for non-experts to understand the details of the results, which are
abstract and academic.
Even with scientific measurements, the problem still exists that facial recognition
accuracy is highly dependent on the scenes it is being used - e.g., how and where the
camera is mounted and what people are wearing or doing that may undermine
performance.
Ethical Concerns
Face recognition has received mainstream media attention because of the ethical
concerns of face recognition, related mass data collection, and privacy concerns.
Moreover, face recognition systems can be used by governments to target minorities
and political enemies for mass detention and intrusive surveillance (1,2,3).
While it is banned in some cities and its use is limited in the EU due to GDPR
(although not banned entirely), companies like Clearview AI offer face recognition
methods with social media networks, to increase the value of face recognition for
public safety/police users.
Face Recognition Analytic Providers
Below is a list of notable face recognition providers, listed alphabetically:
• Amazon: Offers cloud-hosted Rekognition, lets organizations more cheaply
build their own facial recognition systems.
• Anyvision: Very well-funded but controversial startup offers face recognition
for surveillance, access control, and mobile integrations (IPVM Test)

Copyright IPVM 121
• Avigilon: End-to-end surveillance manufacturer, recently added face
recognition (IPVM Test)
• Briefcam: Niche enterprise analytics developer, recently added face
recognition (IPVM Test)
• Dahua: Large China manufacturer, offers face recognition cameras and NVRs
(IPVM Test)
• Hikvision: World's largest surveillance manufacturer, offers face recognition
cameras and NVRs (IPVM Test)
• Megvii/Face++/Sensetime: Major face recognition specialists in China, very
little presence outside of China
• NEC: Most commonly used for government facial recognition projects for face
recognition, requires customized setup.

Copyright IPVM 122
Surveillance Storage 101
This guide teaches the fundamentals of video surveillance storage.
We cover:
• Surveillance Storage
• NVR / Recorder Storage
• Camera / Edge Storage
• Network Storage
• Cloud Storage
• Specialized Surveillance Hard Drives
• Storage Capacity
• Storage Duration
• Recording Types (Continuous vs Event)
• Recording Schedules
• Automatic Storage Cleanup

Copyright IPVM 123
• Edge Redundant Recording / Trickling
• Storage Redundancy
• RAID
• Redundant / Backup Servers
• Notable Surveillance Storage Suppliers
Surveillance Storage
Surveillance video must be stored in order to review past events. Encoded video
streams are saved as video files, where they can be selected and played back in
client decoding software.
Storage can be located in recorders, cameras, network-based, or in the cloud.
NVR / Recorder Storage
Recording with an NVR / VMS Recorder is the most common choice in video
surveillance. Storing video in central storage drives spreads out the storage of
multiple cameras which is more efficient than recording on camera, which reduces
cost.

Copyright IPVM 124
Video is typically stored on one or more traditional hard drives, which can be
configured in RAID which provides video redundancy. RAID is common in large
systems. Systems without data redundancy risk losing recorded video from many
cameras with the failure of a single hard drive.
Camera / Edge Storage
Storing video in a camera can eliminate the need for a separate recorder. Storage is
typically provided with microSD, SSD or Flash drives.
There are two main ways of using this recording:
• Full time/no VMS/NVR: Edge storage may be used as the only storage, with
no central NVR/VMS, sometimes used in small systems.

Copyright IPVM 125
• Redundant recording: Or it may be used for redundancy, recording to the
camera while the network is down and the recorder cannot be reached,
then offloaded when network connectivity is restored.
However, recording in cameras is not common, as it is generally less expensive and
simpler to connect to a recorder that stores the video. Additionally, if a camera is
damaged or stolen, the loss of recorded video is likely.
On the other hand, recording on cameras is a growing trend marketed by a number
of newer entrants, most notably Verkada.

Copyright IPVM 126
Network Storage
Adding network-based storage can expand storage capacity (more hard drives) in a
surveillance system. This can allow a system to support more cameras, store longer
recording times, or increase the resolution/quality of the current cameras.
There are 2 common options for adding network storage; NAS and SAN.
NAS storage is a low-cost option of network-attached storage for small scale
systems. Storage speed and redundancy are lower than Recorder or SAN-based
systems.
Storing video in a SAN offers large scale expansion, supports flexible design
architectures and increased data redundancy.
Adding secondary hardware for storage will typically significantly increase costs
when compared to adding more storage in the primary recorder. Moreover, SANs

Copyright IPVM 127
typically require manufacturer-specific training for higher-level technicians or
engineers to configure and support.
Cloud Storage
Cloud storage has historically been a limited option for video surveillance. While
storing video in the cloud decreases (or eliminates) the need for recorder storage, it
has been primarily offered in SMB-focused limited cloud-based VSaaS.
The primary advantages of cloud storage are eliminating on-site recorders plus
making it easier to add more storage and providing data redundancy.
Additionally, using cloud storage eliminates the risk of video loss if the recorder is
damaged or stolen. This is commonly marketed by cloud storage providers and while
stealing the recorder happens, it is not a common problem.

Copyright IPVM 128
However, because the video is being stored off-premise, the video must be
uploaded, increasing bandwidth consumption and risking loss in an Internet outage.
There are also practical limitations to the number of cameras that can upload video
to the cloud, based on upstream Internet bandwidth for a given site.
Once the video is uploaded to the cloud, it can typically be viewed directly without
connecting to the local cameras/gateway.
It is much less common to store video in the cloud as 77% of integrators state they
infrequently or never do so. See more details in our report, Cloud Video
Surveillance Storage Usage 2021.

VS101-2023.pdf

Recommended

Recommended

More Related Content

Similar to VS101-2023.pdf

Similar to VS101-2023.pdf (20)

More from PawachMetharattanara

More from PawachMetharattanara (20)

Recently uploaded

Recently uploaded (20)

VS101-2023.pdf