The document discusses a technique for detecting human motion in video surveillance using computer vision. It proposes a method called DECOLOR (Detecting Contiguous Outliers in the LOw-rank Representation) that formulates object detection as outlier detection in a low-rank representation of video frames. This allows it to detect moving objects flexibly without assumptions about foreground or background behavior. DECOLOR simultaneously performs object detection and background estimation using only the test video sequence, without requiring training data. The method models the outlier support explicitly and favors spatially contiguous outliers, making it suitable for detecting clustered foreground objects like people. It achieves more accurate detection and background estimation than state-of-the-art robust principal component analysis methods.
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
IRJET- Prediction of Anomalous Activities in a VideoIRJET Journal
The document describes a proposed method for detecting anomalous activities in videos using deep learning models. It uses a convolutional neural network (CNN) to extract spatial features from video frames. These features are then fed into a bidirectional long short-term memory (LSTM) network to learn temporal features across frames. The CNN-bidirectional LSTM model is trained on normal video activities to learn what normal behavior looks like. During testing, activities that deviate from this learned pattern of normalcy will be flagged as anomalous. The method is evaluated on the UCF Crime dataset and is shown to outperform state-of-the-art approaches for anomalous activity recognition in videos.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
The document describes a new technique for interactive full-body motion capture using multiple infrared sensors. It processes data from each sensor independently and then combines the results to enhance flexibility and accuracy. The method aims to maintain real-time performance while improving on issues like limited actor orientation, inaccurate joint tracking, and conflicting data from individual sensors.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Inspection of Suspicious Human Activity in the Crowd Sourced Areas Captured i...IRJET Journal
The document proposes a system to detect suspicious human activity in crowdsourced video captured by surveillance cameras. The system uses Advanced Motion Detection (AMD) to detect moving objects and generate a reliable background model for analysis. A camera connected to a monitoring room would produce alert messages for any detected suspicious activity based on height, time, and body movement constraints. The system aims to automate real-time video processing for security applications like detecting unauthorized access. It extracts human objects from frames and identifies suspicious behavior using the AMD algorithm before sending alerts.
Background differencing algorithm for moving object detection using system ge...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
IRJET- Prediction of Anomalous Activities in a VideoIRJET Journal
The document describes a proposed method for detecting anomalous activities in videos using deep learning models. It uses a convolutional neural network (CNN) to extract spatial features from video frames. These features are then fed into a bidirectional long short-term memory (LSTM) network to learn temporal features across frames. The CNN-bidirectional LSTM model is trained on normal video activities to learn what normal behavior looks like. During testing, activities that deviate from this learned pattern of normalcy will be flagged as anomalous. The method is evaluated on the UCF Crime dataset and is shown to outperform state-of-the-art approaches for anomalous activity recognition in videos.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
The document describes a new technique for interactive full-body motion capture using multiple infrared sensors. It processes data from each sensor independently and then combines the results to enhance flexibility and accuracy. The method aims to maintain real-time performance while improving on issues like limited actor orientation, inaccurate joint tracking, and conflicting data from individual sensors.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Inspection of Suspicious Human Activity in the Crowd Sourced Areas Captured i...IRJET Journal
The document proposes a system to detect suspicious human activity in crowdsourced video captured by surveillance cameras. The system uses Advanced Motion Detection (AMD) to detect moving objects and generate a reliable background model for analysis. A camera connected to a monitoring room would produce alert messages for any detected suspicious activity based on height, time, and body movement constraints. The system aims to automate real-time video processing for security applications like detecting unauthorized access. It extracts human objects from frames and identifies suspicious behavior using the AMD algorithm before sending alerts.
Background differencing algorithm for moving object detection using system ge...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Development of Human Tracking in Video Surveillance System for Activity Anal...IOSR Journals
This document discusses the development of a human tracking system for video surveillance. It proposes a three step process: 1) detecting moving objects through background subtraction and optical flow segmentation, 2) tracking detected humans across frames while handling occlusion, and 3) analyzing activities to trigger alerts for abnormal behaviors. Previous research on human detection, tracking, and occlusion handling is also reviewed. The overall architecture is presented with each step - detection, tracking, and activity analysis - broken down in more detail.
Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.
Deep Learning - a Path from Big Data Indexing to Robotic ApplicationsDarius Burschka
These are the slides to my ShanghAI lecture from Dec 10, 2020. It proposes necessary extensions to make DeepNets appropriate tools for robotic systems.
The talk can be found on https://fb.watch/2hXDC6K4Pq/
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)csandit
The proposed work aims to create a smart application camera, with the intention of eliminating
the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at
arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in
OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen
Faces and the test images are verified by using distance based algorithm against the eigenfaces,
like Euclidean distance algorithm or Mahalanobis Algorithm.
If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an
alarm signal is raised.
Deep-learning based single object tracker for night surveillance IJECEIAES
This document summarizes a research paper that proposes a deep learning-based single object tracker for night surveillance video. The tracker uses pre-trained convolutional neural networks coupled with fully connected layers. The network is trained online during tracking to model changes in the target object's appearance as it moves. The paper tests different hyperparameters for the online learning, including optimization algorithms, learning rates, and ratios of positive and negative training samples, to determine the optimal setup for nighttime tracking. It evaluates the tracker on 14 night surveillance videos and finds that using the Adam optimizer with a learning rate of 0.00075 and a 2:1 ratio of positive to negative samples achieves the best accuracy.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
IRJET- Real Time Video Object Tracking using Motion EstimationIRJET Journal
The document discusses real time video object tracking using motion estimation techniques. It describes using background subtraction, thresholding, background estimation and optical flow to detect and track moving objects in video frames. Morphological operations like dilation and erosion are used for smoothing detected object regions. Dynamic thresholding and mathematical morphology help attenuate color variations from background motions while highlighting moving objects. The algorithm marks pixels as foreground if above a threshold and performs closing and removes small regions. Background is updated adaptively to prevent detection of artificial tails behind moving objects. Correlation of frames improves detection of multiple moving objects with significant contrast changes, even with poor lighting conditions.
IRJET- Behavior Analysis from Videos using Motion based Feature ExtractionIRJET Journal
This document proposes a technique for analyzing human behavior in videos using motion-based feature extraction. It discusses how previous approaches have used spatial and temporal features to detect abnormal behaviors. The proposed approach extracts motion features from videos to represent each video with a single feature vector, rather than extracting features from each individual frame. This reduces the feature space and unnecessary information. The technique involves preprocessing videos into frames, extracting motion features, using KNN classification on the features to classify behaviors as normal or abnormal, and evaluating the method's performance on various metrics like accuracy, recall, and precision. Testing on fight and riot datasets showed the motion-based approach achieved higher accuracy, recall, precision and F-measure than a non-motion based approach.
Robust and Efficient Coupling of Perception to Actuation with Metric and Non-...Darius Burschka
The talk motivates a re-thinking of the way, how perception passes the information to the control modules. Metric information is not a native space of the camera and apparently also not used in biology for navigation. Early abstraction of information from images loses a lot of important information that can be directly used for following (Visual-Servoing), motion estimation(Motion Blurr), and collision relations(Optical Flow Clustering). I present in this talk ways, how we use the image information in "classical way" that does not require any learning and runs on low-power CPUs.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
IRJET- Full Body Motion Detection and Surveillance System ApplicationIRJET Journal
1) The document discusses a system for real-time full-body motion detection and surveillance using computer vision techniques.
2) It involves comparing video frames over time to detect motion by treating videos as stacks of frames and looking for differences between frames.
3) The goal is to track body motion in real time using OpenCV for applications like surveillance systems, pose estimation, and other filters.
Visual Mapping and Collision Avoidance Dynamic Environments in Dynamic Enviro...Darius Burschka
How conventional vision is more appropriate for control since it provides also error analysis. There is a lot of information in the images that is lost when converting to 3D
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Heap graph, software birthmark, frequent sub graph mining.iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
The document describes a smart cap system to help blind people navigate independently. The cap uses a camera to capture the user's surroundings, detects objects using TensorFlow and describes the scene to the user via earphones. It analyzes frames using CNN models and a text-to-speech synthesizer. The system aims to boost confidence for blind users to move freely and identify objects like fruits and vegetables. It provides real-time navigation and notifications of obstacles while converting text to speech. The researchers believe this could help the 285 million visually impaired people live independently.
Threshold based filtering technique for efficient moving object detection and...eSAT Journals
Abstract Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is
used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has
been developed for video object detection, however the object detection accuracy and background object detection in the video
frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and
Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video
file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving
the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects
in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function,
particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking
accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to
enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT
framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with
the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics
such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object
detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve
the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe-
art works.
Keywords: Object segmentation, Object tracking, Object Detection, Enhanced Laplacian Thresholding, Median
Filter, Color Histogram-based Particle Filter
A Survey on Object Detection Methods in Visual Sensor Networks ijassn
Object detection is one of the major challenges in visual sensor networks (VSNs) which is set up in the
monitoring applications. Many approaches proposed to solve the object detection problem in VSNs,
considering diverse metrics such as reliability, energy consumption, detection accuracy and being realtime.
In this paper, a survey on the object detection methods in visual sensor networks is presented for the
first time. Furthermore, this paper classified the methods precisely. Two main object detection categories
in VSNs that explored in this paper are conventional object detection methods and object detection
approaches with the camera nodes involvement. To be more precise, presented survey promotes an
overview of recent object detection methods' literature with their performance evaluation. Also, this
research is challenging and the object detection issue in the visual sensor networks is open caused by
differences in estimations and performance metrics. Therefore, the survey concludes with open research
challenges.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
IRJET- Moving Object Detection with Shadow Compression using Foreground Segme...IRJET Journal
The document describes a study that investigates using foreground segmentation to extract moving objects from videos by detecting differences between frames, with the goal of tracking silhouettes of moving objects to create an interactive video display. The researchers propose using a statistical approach to segment foreground objects and apply filtering techniques to reduce noise from the extracted shadows. The results indicate the extraction process accurately tracks motion and outlines of foreground objects between frames.
IRJET - Real-Time Analysis of Video Surveillance using Machine Learning a...IRJET Journal
This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
Development of Human Tracking in Video Surveillance System for Activity Anal...IOSR Journals
This document discusses the development of a human tracking system for video surveillance. It proposes a three step process: 1) detecting moving objects through background subtraction and optical flow segmentation, 2) tracking detected humans across frames while handling occlusion, and 3) analyzing activities to trigger alerts for abnormal behaviors. Previous research on human detection, tracking, and occlusion handling is also reviewed. The overall architecture is presented with each step - detection, tracking, and activity analysis - broken down in more detail.
Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.
Deep Learning - a Path from Big Data Indexing to Robotic ApplicationsDarius Burschka
These are the slides to my ShanghAI lecture from Dec 10, 2020. It proposes necessary extensions to make DeepNets appropriate tools for robotic systems.
The talk can be found on https://fb.watch/2hXDC6K4Pq/
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)csandit
The proposed work aims to create a smart application camera, with the intention of eliminating
the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at
arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in
OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen
Faces and the test images are verified by using distance based algorithm against the eigenfaces,
like Euclidean distance algorithm or Mahalanobis Algorithm.
If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an
alarm signal is raised.
Deep-learning based single object tracker for night surveillance IJECEIAES
This document summarizes a research paper that proposes a deep learning-based single object tracker for night surveillance video. The tracker uses pre-trained convolutional neural networks coupled with fully connected layers. The network is trained online during tracking to model changes in the target object's appearance as it moves. The paper tests different hyperparameters for the online learning, including optimization algorithms, learning rates, and ratios of positive and negative training samples, to determine the optimal setup for nighttime tracking. It evaluates the tracker on 14 night surveillance videos and finds that using the Adam optimizer with a learning rate of 0.00075 and a 2:1 ratio of positive to negative samples achieves the best accuracy.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
IRJET- Real Time Video Object Tracking using Motion EstimationIRJET Journal
The document discusses real time video object tracking using motion estimation techniques. It describes using background subtraction, thresholding, background estimation and optical flow to detect and track moving objects in video frames. Morphological operations like dilation and erosion are used for smoothing detected object regions. Dynamic thresholding and mathematical morphology help attenuate color variations from background motions while highlighting moving objects. The algorithm marks pixels as foreground if above a threshold and performs closing and removes small regions. Background is updated adaptively to prevent detection of artificial tails behind moving objects. Correlation of frames improves detection of multiple moving objects with significant contrast changes, even with poor lighting conditions.
IRJET- Behavior Analysis from Videos using Motion based Feature ExtractionIRJET Journal
This document proposes a technique for analyzing human behavior in videos using motion-based feature extraction. It discusses how previous approaches have used spatial and temporal features to detect abnormal behaviors. The proposed approach extracts motion features from videos to represent each video with a single feature vector, rather than extracting features from each individual frame. This reduces the feature space and unnecessary information. The technique involves preprocessing videos into frames, extracting motion features, using KNN classification on the features to classify behaviors as normal or abnormal, and evaluating the method's performance on various metrics like accuracy, recall, and precision. Testing on fight and riot datasets showed the motion-based approach achieved higher accuracy, recall, precision and F-measure than a non-motion based approach.
Robust and Efficient Coupling of Perception to Actuation with Metric and Non-...Darius Burschka
The talk motivates a re-thinking of the way, how perception passes the information to the control modules. Metric information is not a native space of the camera and apparently also not used in biology for navigation. Early abstraction of information from images loses a lot of important information that can be directly used for following (Visual-Servoing), motion estimation(Motion Blurr), and collision relations(Optical Flow Clustering). I present in this talk ways, how we use the image information in "classical way" that does not require any learning and runs on low-power CPUs.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
IRJET- Full Body Motion Detection and Surveillance System ApplicationIRJET Journal
1) The document discusses a system for real-time full-body motion detection and surveillance using computer vision techniques.
2) It involves comparing video frames over time to detect motion by treating videos as stacks of frames and looking for differences between frames.
3) The goal is to track body motion in real time using OpenCV for applications like surveillance systems, pose estimation, and other filters.
Visual Mapping and Collision Avoidance Dynamic Environments in Dynamic Enviro...Darius Burschka
How conventional vision is more appropriate for control since it provides also error analysis. There is a lot of information in the images that is lost when converting to 3D
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Heap graph, software birthmark, frequent sub graph mining.iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
The document describes a smart cap system to help blind people navigate independently. The cap uses a camera to capture the user's surroundings, detects objects using TensorFlow and describes the scene to the user via earphones. It analyzes frames using CNN models and a text-to-speech synthesizer. The system aims to boost confidence for blind users to move freely and identify objects like fruits and vegetables. It provides real-time navigation and notifications of obstacles while converting text to speech. The researchers believe this could help the 285 million visually impaired people live independently.
Threshold based filtering technique for efficient moving object detection and...eSAT Journals
Abstract Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is
used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has
been developed for video object detection, however the object detection accuracy and background object detection in the video
frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and
Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video
file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving
the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects
in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function,
particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking
accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to
enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT
framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with
the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics
such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object
detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve
the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe-
art works.
Keywords: Object segmentation, Object tracking, Object Detection, Enhanced Laplacian Thresholding, Median
Filter, Color Histogram-based Particle Filter
A Survey on Object Detection Methods in Visual Sensor Networks ijassn
Object detection is one of the major challenges in visual sensor networks (VSNs) which is set up in the
monitoring applications. Many approaches proposed to solve the object detection problem in VSNs,
considering diverse metrics such as reliability, energy consumption, detection accuracy and being realtime.
In this paper, a survey on the object detection methods in visual sensor networks is presented for the
first time. Furthermore, this paper classified the methods precisely. Two main object detection categories
in VSNs that explored in this paper are conventional object detection methods and object detection
approaches with the camera nodes involvement. To be more precise, presented survey promotes an
overview of recent object detection methods' literature with their performance evaluation. Also, this
research is challenging and the object detection issue in the visual sensor networks is open caused by
differences in estimations and performance metrics. Therefore, the survey concludes with open research
challenges.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
IRJET- Moving Object Detection with Shadow Compression using Foreground Segme...IRJET Journal
The document describes a study that investigates using foreground segmentation to extract moving objects from videos by detecting differences between frames, with the goal of tracking silhouettes of moving objects to create an interactive video display. The researchers propose using a statistical approach to segment foreground objects and apply filtering techniques to reduce noise from the extracted shadows. The results indicate the extraction process accurately tracks motion and outlines of foreground objects between frames.
IRJET - Real-Time Analysis of Video Surveillance using Machine Learning a...IRJET Journal
This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
Real Time Detection of Moving Object Based on Fpgaiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
1. The document describes a method for real-time detection of moving objects based on background subtraction and its implementation on an FPGA. A static camera is used to capture video frames. The first frame is used as the reference background frame. Pixels in subsequent frames are compared to the background frame and objects are detected where pixel differences exceed a threshold.
2. The method was tested on sample surveillance videos. Background subtraction accurately detected moving objects in test videos in real-time. Future work may include identifying objects using face or palm recognition and activity recognition for visual surveillance applications.
IRJET- Moving Object Detection using Foreground Detection for Video Surveil...IRJET Journal
This document summarizes a research paper that proposes a new method for detecting moving objects in videos using foreground detection and background subtraction. The key steps of the proposed method include initializing a background model using the median of initial frames, dynamically updating the background model to adapt to lighting changes, subtracting the background model from current frames and applying a threshold to detect moving objects, and using morphological operations and projection analysis to extract human bodies and remove noise. The experimental results showed that the proposed method can accurately and reliably detect moving human bodies in real-time video surveillance.
1. The document discusses a visual surveillance system that uses two approaches for detecting moving objects in video streams: a self-organizing background subtraction method (SOBS) and a traditional background subtraction method.
2. SOBS uses an artificial neural network model that learns pixel trajectories over time to automatically generate a background model, allowing it to adapt to scene changes like moving backgrounds or lighting variations.
3. The traditional background subtraction method detects moving regions by calculating the difference between the current frame and a reference background image and updating the background model in real-time using a threshold and filtering techniques.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Robust techniques for background subtraction in urbantaylor_1313
Robust techniques for background subtraction in urban traffic video aim to identify moving objects from video sequences. The paper surveys and compares various background subtraction algorithms, including simple techniques like frame differencing and adaptive median filtering, as well as more sophisticated probabilistic modeling. Experiments show that while complex techniques often perform best, simple adaptive median filtering produces good results with much lower computational complexity for detecting vehicles and pedestrians in traffic video.
DYNAMIC ENERGY MANAGEMENT USING REAL TIME OBJECT DETECTIONIRJET Journal
This document discusses a system for dynamic energy management using real-time object detection. The system divides an area into four sectors and uses the YOLO CV2 algorithm to detect humans in each sector using a Raspberry Pi 4 and webcam. When a human is detected in a particular sector, only the electrical devices in that sector are turned on, minimizing energy usage. The methodology first uses YOLO CV2 for human detection, then implements sector-based electrical control using a Raspberry Pi 4 and hardware components. Dividing the area into sectors allows more granular energy savings compared to controlling an entire area or individual devices.
Moving object detection using background subtraction algorithm using simulinkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Convolutional Neural Network Based Real Time Object Detection Using YOLO V4IRJET Journal
This document describes a project to develop a real-time object detection system using convolutional neural networks (CNNs) implemented in MATLAB. The system will leverage deep learning techniques like CNNs to accurately detect and localize objects in real-time video streams. The key objectives are to achieve state-of-the-art accuracy and reliability in object detection with minimal false positives/negatives, while also enabling real-time performance by processing video frames swiftly. The document provides background on CNNs and object detection, and outlines the proposed system architecture which includes collecting an annotated dataset, preprocessing the data, training a CNN model, and evaluating model performance on a test set.
The document describes a proposed system for real-time object tracking and learning using template matching. The system uses a live video stream and enables the tracking, learning, and detection of real-time objects. It selects an object of interest via cropping and then tracks it with a bounding box. Template matching is used to match the selected object with regions of interest in subsequent frames to mark its location. If a match is found, principal component analysis is used. The system also introduces a PN discrimination algorithm using background subtraction to increase frame processing speed and improve template matching accuracy. This allows the system to overcome limitations of existing methods and enable long-term, real-time object tracking.
This document presents a study on object detection using SSD-MobileNet. The researchers developed a lightweight object detection model using SSD-MobileNet that can perform real-time object detection on embedded systems with limited processing resources. They tested the model on images and video captured using webcams. The model was able to detect objects like people, cars, and animals with good accuracy. The SSD-MobileNet framework provides fast and efficient object detection for applications like autonomous driving assistance systems that require real-time performance on low-power devices.
IRJET- Border Security using Computer VisionIRJET Journal
1. The document describes a computer vision system for border security that uses image processing techniques to automatically detect, track, and destroy targets.
2. The system uses a video camera to capture images that are processed on a computer using MATLAB. Targets can be automatically tracked using image processing algorithms or manually selected by a user.
3. Once a target is selected, the microcontroller controls a mounted gun to track and potentially shoot the target. The goal of the system is to secure borders automatically while reducing human effort.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)cscpconf
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Moving Object Detection for Video SurveillanceIJMER
Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis.
IRJET- Criminal Recognization in CCTV Surveillance VideoIRJET Journal
This document presents research on criminal recognition in CCTV surveillance videos using deep learning. It proposes a method where a user can upload faces of known criminals. When CCTV footage is recorded, the application will monitor for these faces. If a face is recognized, the CCTV camera will track the identified person through multiple cameras by alerting other cameras. The system segments video into images, acquires images, recognizes human faces, constructs motion flows between cameras to track individuals. Experimental results on a dataset show the system's ability to extract patterns from faces and cluster images of different angled faces. The system aims to identify criminals across surveillance camera networks.
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TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
Data Communication and Computer Networks Management System Project Report.pdfKamal Acharya
Networking is a telecommunications network that allows computers to exchange data. In
computer networks, networked computing devices pass data to each other along data
connections. Data is transferred in the form of packets. The connections between nodes are
established using either cable media or wireless media.
Better Builder Magazine brings together premium product manufactures and leading builders to create better differentiated homes and buildings that use less energy, save water and reduce our impact on the environment. The magazine is published four times a year.