Over recent years, surveillance camera is attracting attention due to its wide range of applications in suspicious activity detection. Current surveillance system focuses on analysing
past incidents. This paper proposes an intelligent system for real-time monitoring with added functionality of anticipating the outcome through various Image processing techniques. As this is a sensitive matter, human decisions are given priority, still facilitating limited logical intervention of human resource. This framework detects risk in the area under surveillance. One such dangerous circumstance is implemented, like a person with a knife. Here the prediction is
that in the firm places like ATM, Banks, Offices etc. a person possessing knife is unusual and likely to cause harmful activities like threatening, injuring and stabbing. The experiment demonstrates the effectiveness of the technique on training dataset collected from distinct
environments. An interface is developed to notify concerned authority that boosts reliability and overall accuracy.
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.
A real time aggressive human behaviour detection system in cage environment a...Journal Papers
This document proposes a real-time system called GuARD that detects aggressive human behavior across multiple cameras in an enclosed cage environment. The system uses background subtraction, perspective correction, scale correction, and a cooperative detection scheme across cameras to identify aggressive behavior regions despite challenges like fish-eye lenses, low resolution, multiple people, and low lighting. Experimental results showed the system can successfully identify aggressive behaviors in real-time even on low-end computers.
Pixel Based Fusion Methods for Concealed Weapon DetectionIJERA Editor
Concealed Weapon Detection(CWD) is the detection of weapons underneath a person’s clothing which is an important obstacle for the security of general public as well as safety of public assets like airports and buildings. Concealed weapons such as handbags , knives and explosives are detected using manual screening procedures. It is desirable to detect the concealed weapons from a far off distance at airports and other secured places. A number of sensors with different phenomenology have been developed to observe objects underneath’s persons clothing. As no single technology provide improved performance in CWD applications, different image fusion schemes based on pixel level is proposed . Image obtained from visual camera does not reveal any information hidden under persons clothing whereas MWM image obtained from MWM (Millimeter Wave Imaging )sensor reveal clothing penetration underneath persons cloth but cannot identify the person. In this paper fusion of MWM image with visible image based on pixels is proposed. Experimental results reveal that fused image can identify the person with concealed weapons. Performance metrics such as standard deviation, entropy and cross entropy is calculated and from simulation results it is observed that PCA based fusion method is similar to DWT based fusion scheme.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Survey on video object detection & trackingijctet
This document summarizes previous work on video object detection and tracking techniques. It discusses research papers that used techniques like active contour modeling, gradient-based attraction fields, neural fuzzy networks, and region-based contour extraction for object tracking. Background subtraction, frame differencing, optical flow, spatio-temporal features, Kalman filtering, and contour tracking are described as common video object detection techniques. The challenges of multi-object data association and state estimation for tracking multiple objects are also mentioned.
A multi-task learning based hybrid prediction algorithm for privacy preservin...journalBEEI
There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
IRJET- Comparative Analysis of Video Processing Object DetectionIRJET Journal
This document summarizes research on comparative analysis of video processing object detection techniques. It begins with an abstract describing the goal of object detection in images and videos and challenges involved. It then discusses benefits of object detection and provides a literature review summarizing the approaches of 15 other research papers on object detection, including approaches using background subtraction, segmentation, feature extraction and deep learning algorithms. The document concludes by stating that object detection has wide applications and research is ongoing to improve accuracy and robustness of detection.
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
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.
A real time aggressive human behaviour detection system in cage environment a...Journal Papers
This document proposes a real-time system called GuARD that detects aggressive human behavior across multiple cameras in an enclosed cage environment. The system uses background subtraction, perspective correction, scale correction, and a cooperative detection scheme across cameras to identify aggressive behavior regions despite challenges like fish-eye lenses, low resolution, multiple people, and low lighting. Experimental results showed the system can successfully identify aggressive behaviors in real-time even on low-end computers.
Pixel Based Fusion Methods for Concealed Weapon DetectionIJERA Editor
Concealed Weapon Detection(CWD) is the detection of weapons underneath a person’s clothing which is an important obstacle for the security of general public as well as safety of public assets like airports and buildings. Concealed weapons such as handbags , knives and explosives are detected using manual screening procedures. It is desirable to detect the concealed weapons from a far off distance at airports and other secured places. A number of sensors with different phenomenology have been developed to observe objects underneath’s persons clothing. As no single technology provide improved performance in CWD applications, different image fusion schemes based on pixel level is proposed . Image obtained from visual camera does not reveal any information hidden under persons clothing whereas MWM image obtained from MWM (Millimeter Wave Imaging )sensor reveal clothing penetration underneath persons cloth but cannot identify the person. In this paper fusion of MWM image with visible image based on pixels is proposed. Experimental results reveal that fused image can identify the person with concealed weapons. Performance metrics such as standard deviation, entropy and cross entropy is calculated and from simulation results it is observed that PCA based fusion method is similar to DWT based fusion scheme.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Survey on video object detection & trackingijctet
This document summarizes previous work on video object detection and tracking techniques. It discusses research papers that used techniques like active contour modeling, gradient-based attraction fields, neural fuzzy networks, and region-based contour extraction for object tracking. Background subtraction, frame differencing, optical flow, spatio-temporal features, Kalman filtering, and contour tracking are described as common video object detection techniques. The challenges of multi-object data association and state estimation for tracking multiple objects are also mentioned.
A multi-task learning based hybrid prediction algorithm for privacy preservin...journalBEEI
There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
IRJET- Comparative Analysis of Video Processing Object DetectionIRJET Journal
This document summarizes research on comparative analysis of video processing object detection techniques. It begins with an abstract describing the goal of object detection in images and videos and challenges involved. It then discusses benefits of object detection and provides a literature review summarizing the approaches of 15 other research papers on object detection, including approaches using background subtraction, segmentation, feature extraction and deep learning algorithms. The document concludes by stating that object detection has wide applications and research is ongoing to improve accuracy and robustness of detection.
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
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.
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.
Human Motion Detection in Video Surveillance using Computer Vision TechniqueIRJET Journal
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.
Face detection and recognition has been prevalent with research scholars and diverse approaches have been
incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body
scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems
deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images
and video image to be used for detection and recognition. This led to newer methods for face detection and recognition
to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as
Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA),
have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks
based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear
faces with an acceptance ratio of more than 90% and execution time of only few seconds.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
The Basic Idea Behind “Smart Web Cam Motion Detection Surveillance System” Is To Stop The Intruder To Getting Into The Place Where A High End Security Is Required. This Paper Proposes A Method For Detecting The Motion Of A Particular Object Being Observed. The Motion Tracking Surveillance Has Gained A Lot Of Interests Over Past Few Years. This System Is Brought Into Effect Providing Relief To The Normal Video Surveillance System Which Offers Time-Consuming Reviewing Process. Through The Study And Evaluation Of Products, We Propose A Motion Tracking Surveillance System Consisting Of Its Method For Motion Detection And Its Own Graphic User Interface.
The document proposes a secure personal identification system based on human retina recognition. The system uses retinal vascular patterns, which are unique to each individual, for identification. It consists of three stages: 1) preprocessing to extract the vascular pattern from retinal images, 2) feature extraction to identify feature points like endings and bifurcations, and represent them as vectors, and 3) matching input images to templates by calculating distances between feature vectors. Experimental results on two retinal image databases achieved over 97% accuracy, demonstrating the potential of the proposed system for high-security identification.
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.
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET Journal
This document summarizes research on object detection techniques using AI and deep learning. It discusses how object detection can be used to enhance e-commerce by recommending products seen in videos. The document reviews several existing object detection algorithms and methods, including YOLO and topological maps. It also identifies limitations in existing systems for multi-class detection efficiency and handling similar backgrounds. The researchers propose using object detection with video input for product recommendations and improving current systems.
Application To Monitor And Manage People In Crowded Places Using Neural NetworksIJSRED
The document describes a proposed system to monitor crowds in public places using neural networks and computer vision. The system would use a camera to capture video feeds of areas like temples or company events. An object detection model trained on neural networks would detect and track humans in the video. It would count the number of people and control entry gates as needed to avoid overcrowding. The proposed system architecture includes components for video capture, object detection/tracking using a neural network model, data storage, application control interface, and GUI display. It then outlines the object detection and tracking process which involves detecting new objects, associating IDs to tracked objects, and deregistering lost objects. The output shows sample terminal outputs of the system initializing, tracking people
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
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.
IRJET-Real-Time Object Detection: A SurveyIRJET Journal
This document provides an overview of real-time object detection techniques. It discusses several challenges in detecting objects including illumination variation, moving object appearance changes, abrupt motion, occlusion, shadows, and problems related to cameras. The document then reviews several existing object detection methods and algorithms. These include techniques using color segmentation, edge tracking, shape context features, image segmentation, and support vector machines or k-nearest neighbor classifiers applied to features like GIST or SIFT. The goal of the literature review is to analyze different object recognition and segmentation approaches that could be applied for real-time object detection.
This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
This document discusses human action recognition from images and videos. It proposes using web-based classifiers to incrementally collect action images without human labeling. These images are used to build action models in an unsupervised manner and annotate human actions in videos. The key contributions are proposing a system to collect action images from the web using text queries, building action models from these images, and using the models to annotate actions in uncontrolled videos like YouTube videos.
Real Time Crime Detection using Deep LearningIRJET Journal
The document discusses the application of deep learning techniques for real-time crime detection. It provides an overview of various deep learning architectures and methods used, including CNNs, LSTMs, YOLO, ResNet50, and R-CNN models. These techniques are applied to analyze data sources like surveillance footage and social media to identify criminal activities. The paper also examines challenges in real-time crime detection using deep learning and discusses ethical considerations. Key deep learning models discussed are Spot Crime (which uses CNNs for behavior classification), YOLO (for object detection), and combining ResNet50 and LSTM for crime classification in videos.
The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
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.
A Novel Background Subtraction Algorithm for Person Tracking Based on K-NN cscpconf
Object tracking can be defined as the process of detecting an object of interest from a video
scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers
are getting attracted in the field of computer vision, specifically the field of object tracking in
video surveillance. The main purpose of this paper is to give to the reader information of the
present state of the art object tracking, together with presenting steps involved in Background
Subtraction and their techniques. In related literature we found three main methods of object
tracking: the first method is the optical flow; the second is related to the background
subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current
frame with the background model that we have set before, so we can classified each pixel of the
image as a foreground or a background element, then comes the tracking step to present our
object of interest, which is a person, by his centroid. The tracking step is divided into two
different methods, the surface method and the K-NN method, both are explained in the paper.
Our proposed method is implemented and evaluated using CAVIAR database.
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.
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.
Human Motion Detection in Video Surveillance using Computer Vision TechniqueIRJET Journal
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.
Face detection and recognition has been prevalent with research scholars and diverse approaches have been
incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body
scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems
deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images
and video image to be used for detection and recognition. This led to newer methods for face detection and recognition
to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as
Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA),
have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks
based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear
faces with an acceptance ratio of more than 90% and execution time of only few seconds.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
The Basic Idea Behind “Smart Web Cam Motion Detection Surveillance System” Is To Stop The Intruder To Getting Into The Place Where A High End Security Is Required. This Paper Proposes A Method For Detecting The Motion Of A Particular Object Being Observed. The Motion Tracking Surveillance Has Gained A Lot Of Interests Over Past Few Years. This System Is Brought Into Effect Providing Relief To The Normal Video Surveillance System Which Offers Time-Consuming Reviewing Process. Through The Study And Evaluation Of Products, We Propose A Motion Tracking Surveillance System Consisting Of Its Method For Motion Detection And Its Own Graphic User Interface.
The document proposes a secure personal identification system based on human retina recognition. The system uses retinal vascular patterns, which are unique to each individual, for identification. It consists of three stages: 1) preprocessing to extract the vascular pattern from retinal images, 2) feature extraction to identify feature points like endings and bifurcations, and represent them as vectors, and 3) matching input images to templates by calculating distances between feature vectors. Experimental results on two retinal image databases achieved over 97% accuracy, demonstrating the potential of the proposed system for high-security identification.
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.
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET Journal
This document summarizes research on object detection techniques using AI and deep learning. It discusses how object detection can be used to enhance e-commerce by recommending products seen in videos. The document reviews several existing object detection algorithms and methods, including YOLO and topological maps. It also identifies limitations in existing systems for multi-class detection efficiency and handling similar backgrounds. The researchers propose using object detection with video input for product recommendations and improving current systems.
Application To Monitor And Manage People In Crowded Places Using Neural NetworksIJSRED
The document describes a proposed system to monitor crowds in public places using neural networks and computer vision. The system would use a camera to capture video feeds of areas like temples or company events. An object detection model trained on neural networks would detect and track humans in the video. It would count the number of people and control entry gates as needed to avoid overcrowding. The proposed system architecture includes components for video capture, object detection/tracking using a neural network model, data storage, application control interface, and GUI display. It then outlines the object detection and tracking process which involves detecting new objects, associating IDs to tracked objects, and deregistering lost objects. The output shows sample terminal outputs of the system initializing, tracking people
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
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.
IRJET-Real-Time Object Detection: A SurveyIRJET Journal
This document provides an overview of real-time object detection techniques. It discusses several challenges in detecting objects including illumination variation, moving object appearance changes, abrupt motion, occlusion, shadows, and problems related to cameras. The document then reviews several existing object detection methods and algorithms. These include techniques using color segmentation, edge tracking, shape context features, image segmentation, and support vector machines or k-nearest neighbor classifiers applied to features like GIST or SIFT. The goal of the literature review is to analyze different object recognition and segmentation approaches that could be applied for real-time object detection.
This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
This document discusses human action recognition from images and videos. It proposes using web-based classifiers to incrementally collect action images without human labeling. These images are used to build action models in an unsupervised manner and annotate human actions in videos. The key contributions are proposing a system to collect action images from the web using text queries, building action models from these images, and using the models to annotate actions in uncontrolled videos like YouTube videos.
Real Time Crime Detection using Deep LearningIRJET Journal
The document discusses the application of deep learning techniques for real-time crime detection. It provides an overview of various deep learning architectures and methods used, including CNNs, LSTMs, YOLO, ResNet50, and R-CNN models. These techniques are applied to analyze data sources like surveillance footage and social media to identify criminal activities. The paper also examines challenges in real-time crime detection using deep learning and discusses ethical considerations. Key deep learning models discussed are Spot Crime (which uses CNNs for behavior classification), YOLO (for object detection), and combining ResNet50 and LSTM for crime classification in videos.
The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
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.
A Novel Background Subtraction Algorithm for Person Tracking Based on K-NN cscpconf
Object tracking can be defined as the process of detecting an object of interest from a video
scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers
are getting attracted in the field of computer vision, specifically the field of object tracking in
video surveillance. The main purpose of this paper is to give to the reader information of the
present state of the art object tracking, together with presenting steps involved in Background
Subtraction and their techniques. In related literature we found three main methods of object
tracking: the first method is the optical flow; the second is related to the background
subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current
frame with the background model that we have set before, so we can classified each pixel of the
image as a foreground or a background element, then comes the tracking step to present our
object of interest, which is a person, by his centroid. The tracking step is divided into two
different methods, the surface method and the K-NN method, both are explained in the paper.
Our proposed method is implemented and evaluated using CAVIAR database.
The complete human body or the various limb postures are involved in human action. These days,
Abnormal Human Activity Recognition (Abnormal HAR) is highly well noticed and surveyed in many
studies. However, because of complicated difficulties such as sensor movement, positioning, and so on,
as well as how individuals carry out their activities, it continues to be a difficult process. Identifying
particular activities benefits human-centric applications such as postoperative trauma recovery, gesture
detection, exercise, fitness, and home care help. The HAR system has the ability to automate or
simplify most of the people’s everyday chores. HAR systems often use supervised or unsupervised
learning as their foundation. Unsupervised systems operate according to a set of rules, whereas
supervised systems need to be trained beforehand using specific datasets. This study conducts detailed
literature reviews on the development of various activity identification techniques currently being used.
The three methods—wearable device-based, pose-based, and smartphone sensor—are examined in this
inquiry for identifying abnormal acts (AAD). The sensors in wearable devices collect data, whereas the
gyroscopes and accelerometers in smartphones provide input to the sensors in wearable devices. To
categorize activities, pose estimation uses a neural network. The Anomalous Action Detection Dataset
(Ano-AAD) is created and improved using several methods. The study examines fresh datasets and
innovative models, including UCF-Crime. A new pattern in anomalous HAR systems has emerged,
linking anomalous HAR tasks to computer vision applications including security, video surveillance,
and home monitoring. In terms of issues and potential solutions, the survey looks at visionbased HAR.
A Robot Collision Avoidance Method Using Kinect and Global VisionTELKOMNIKA JOURNAL
This document describes a robot collision avoidance method using Kinect and global vision. Global vision is installed above the robot to detect potential collision objects in real-time using background subtraction and thresholding algorithms. Kinect detects human skeletons and uses a Kalman filter to predict joint positions over time. The predicted joint positions are used to calculate a human joint danger index to evaluate collision risk levels. Different motion control strategies are then applied depending on the obstacle category and danger index value.
A NOVEL METHOD FOR PERSON TRACKING BASED K-NN : COMPARISON WITH SIFT AND MEAN...sipij
Object tracking can be defined as the process of detecting an object of interest from a video scene and
keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a
challenging problem and it’s an important task. Many researchers are getting attracted in the field of
computer vision, specifically the field of object tracking in video surveillance. The main purpose of this
paper is to give to the reader information of the present state of the art object tracking, together with
presenting steps involved in Background Subtraction and their techniques. In related literature we found
three main methods of object tracking: the first method is the optical flow; the second is related to the
background subtraction, which is divided into two types presented in this paper, then the temporal
differencing and the SIFT method and the last one is the mean shift method. We present a novel approach
to background subtraction that compare a current frame with the background model that we have set
before, so we can classified each pixel of the image as a foreground or a background element, then comes
the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is
divided into two different methods, the surface method and the K-NN method, both are explained in the
paper. Our proposed method is implemented and evaluated using CAVIAR database.
Abnormal activity detection in surveillance video scenesTELKOMNIKA JOURNAL
- The document presents an intelligent framework for detecting abnormal human activity in surveillance videos of an academic environment.
- The framework consists of two main processes: 1) a tracking system that identifies and tracks targets while extracting features to understand human activity, and 2) a decision system that classifies activity as normal or abnormal and triggers an alarm for abnormal activities.
- The key steps are preprocessing videos, detecting moving objects, segmenting images, extracting features, tracking targets, and classifying activities as normal (walking) or abnormal (falling, boxing, waving) using KNN. Alarms are generated for recognized abnormal activities.
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
A fully integrated violence detection system using CNN and LSTM IJECEIAES
The document describes a proposed violence detection system that uses convolutional neural networks (CNN) and long short-term memory (LSTM). The system is intended to analyze real-time video footage to detect violent events and notify authorities. It uses a pre-trained Xception model for spatial feature extraction of video frames, which are then fed into an LSTM network to learn temporal relationships between frames. The system was tested on benchmark datasets and achieved 98.32% accuracy on the Movies dataset and 96.55% accuracy on the Hockey dataset. A mobile application was also developed to allow authorities to monitor video feeds and receive alerts of detected violence to respond quickly.
Robust Motion Detection and Tracking of Moving Objects using HOG Feature and ...CSCJournals
Detection and tracking of moving objects has gained significant importance due to intense technological progress in the field of computer science dealing with video surveillance systems. Human motion is generally nonlinear and non-Gaussian and thus many algorithms are not suitable for tracking. One of the applications to maintain universal security is crowd control. The main problem of video surveillance is continuous monitoring with regard to crime prevention. For security monitoring of live surveillance systems, target identification and tracking strategies can automatically send warnings to monitoring officers. In this paper, we propose a robust tracking of a specified person using the individuals' feature. The proposed method to determine automatic detection and tracking combines Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The Histogram oriented Gradient features are applied to single detection window for the identification of human area, after we use particle filters for robust specific people tracking using color and skin color based on the characteristics of a target individual. We have been improving the implementation, evaluation system of our proposed methods. In our systems, for experiments, we choose structured crowded scenes. From our experimental results, we have achieved high accuracy detection rates and robust motion tracking for specific targets.
The detection of human beings in a camera attracts more attention because of its wide range of applications such as abnormal event detection, person counting in a dense crowd, person identification, fall detection for care to elderly people, etc. Over the time, various techniques have evolved to enhance the visual information. This article presents a novel 3-D intelligent information system for identifying abnormal human activity using background subtraction, rectification, morphology, neural networks and depth estimation with a thermal camera and a pair of hand held Universal Serial Bus (USB) camera to visualize un-calibrated images. The proposed system detects strongest points using Speed-Up Robust Features (SURF). The Sum of Absolute Difference (SAD) algorithm match the strongest points detected by SURF. 3-D object model and image stitching from image sequences are carried out in the proposed work. A series of images captured from different cameras are stitched into a geometrically consistent mosaic either horizontally/vertically based on the image acquisition. 3-D image and depth estimation of un-calibrated stereo images are acquired using rectification and disparity. The background is separated from the scene using threshold approach. Features are extracted using morphological operators in order to get the skeleton. Junction points and end points of the skeleton image are obtained from the skeleton. Data set of abnormal human activity is created using supervised learning such as neural network with a thermal camera and a pair of webcam. The feature vector of an activity is compared with already created data set, if a match occurs the classifier detects abnormal human activity. Additionally the proposed algorithm performs depth estimation to measure real time distance of objects dynamically. The system use thermal camera, Intel computing stick, converter, video graphics array (VGA) to high-definition multimedia interface (HDMI) and webcams. The proposed novel intelligent information system gives 94% maximum accuracy and 89% minimum accuracy for different activities, thus it effectively detects suspicious activity during day and night.
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.
Java Implementation based Heterogeneous Video Sequence Automated Surveillance...CSCJournals
Automated video based surveillance monitoring is an essential and computationally challenging task to resolve issues in the secure access localities. This paper deals with some of the issues which are encountered in the integration surveillance monitoring in the real-life circumstances. We have employed video frames which are extorted from heterogeneous video formats. Each video frame is chosen to identify the anomalous events which are occurred in the sequence of time-driven process. Background subtraction is essentially required based on the optimal threshold and reference frame. Rest of the frames are ablated from reference image, hence all the foreground images paradigms are obtained. The co-ordinate existing in the deducted images is found by scanning the images horizontally until the occurrence of first black pixel. Obtained coordinate is twinned with existing co-ordinates in the primary images. The twinned co-ordinate in the primary image is considered as an active-region-of-interest. At the end, the starred images are converted to temporal video that scrutinizes the moving silhouettes of human behaviors in a static background. The proposed model is implemented in Java. Results and performance analysis are carried out in the real-life environments.
Motion detection is the process of detecting moving objects in background images. Motion detection plays a fundamental role in any object tracking or video surveillance algorithm. The reliability with which potential foreground objects in movement can be identified, directly impacts on the efficiency and performance level achievable by subsequent processing stages of tracking or object recognition. The system automatically performs a task and gives alert to security in an area. This paper represents review on Motion detection is an essential for many video applications such as video surveillance, military reconnaissance, and robotics. Most of these applications demand low power consumption, compact and lightweight design, and high speed computation platform for processing image data in real time. Miss. Aditi Kumbhar | Dr. Pradip Bhaskar"A Review on Motion Detection Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5928.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/5928/a-review-on-motion-detection-techniques/miss-aditi-kumbhar
The document provides an introduction to human action recognition (HAR). It discusses the background and challenges of HAR, including spatial and lighting variations, occlusions, background clutter, and more. It also covers the applications of HAR in areas like kinesiology, computer graphics, behavioral biometrics, video analysis, surveillance, and human-computer interaction. Finally, it discusses techniques for HAR including local and holistic representations and the use of semantic features to recognize activities.
Background Subtraction Algorithm Based Human Behavior DetectionIJERA Editor
This document summarizes a research paper on developing a new video surveillance system to detect human behavior in real-time video streams. It discusses background subtraction as an effective technique for moving object detection. The proposed system applies background subtraction, thresholding, morphological operations and object tracking to detect both normal and abnormal human behaviors. Experimental results show the system can efficiently track humans and detect abnormal activities in video streams.
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.
Similar to SURVEILLANCE VIDEO BASED ROBUST DETECTION AND NOTIFICATION OF REAL TIME SUSPICIOUS ACTIVITIES IN INDOOR SCENARIOS (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
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2. 228 Computer Science & Information Technology (CS & IT)
movements [9-12] and object properties present in the video frame sequences [8]. To estimate the
possibility of danger caused by any physical force is well identified in this paper by recognizing
the sharp and harmful weapons, hand prehensile and grasping movements. A person being
attacked by a knife in an environment of more than one individual is the major concern of our
research project.
The proposed framework is a novel approach to detect and notify indoor violence like stabbing,
thrashing or any activity involving physical force, to the concerned authority. The
implementation is useful for examining such suspicious activity in enclosed places like ATM,
Classroom, theatre, houses etc. A drastic change is brought in the society by introducing
technology in enhancing the security system prevailing in the present world. The system is also
dynamic in sending message to the relevant authority for undertaking precautionary measures or
enforcing the desired action.
2. RELATED WORK
The influencing factor to the experiment is the benefit of social security that is foreseen by
enhancing the functionality of Visual surveillance camera by embedding the provision of
processing, detecting and notifying the menace befallen by the person who is captured in its input
video.
We are interested in the previous work that come from the field of human interaction and motion
tracking in order to qualitatively analyse the real time human motion and predict the ongoing
activities in contact with other human being present and with the objects. Most of these work use
background subtraction practice to get the binary foreground image. [5][6][3].
A vast amount of literature exists on real time hand tracking. Few are picked as follows: Javeria
Farooq et al. [12], Milad Rafiee Vahid et al. [16], P Raghu Veera Chowdary et al. [9], Mykyta
Kovalenko et al. [10] and Pedro Cisneros et al. [11] provide information to the logic of the
experiment for capturing the real time hand movement data and processing them using matching,
computing and anticipation techniques to get the actual hand behavioural understanding that
helps in decision making of the scenario under test. We are interested in works from pattern
recognition [13] and comparison for identifying the hand posture and comparing it with the data
base images [14]. Also, the knowledge with respect to hand prehensile and clutching movements
are acquired that is used in the apprehensive area for identifying the hand mould to conclude the
presence of object in hand.
Although, Human activity processing and hand-object interaction analysis is a difficult task from
a machine vision perspective it has extensive benefit to the Societal security and brings a drastic
awareness in the current system to prevent offensive conduct in public space.
The approach is to recognise the presence of sharp object in hand which is one of the means to
cause harm. The shape, orientation and projection of the hand held object is to the discovered.
Radu-Daniel Vatavu et al. [15] conducted an investigation on the feasibility of using the posture
of the hand during prehension in order to identify geometric properties of grasped objects such as
size and shape. Garg et al. [8] suggested an image segmentation technique to recognize the hand
object in a scene using Mathematical analysis and locate the object position in the Scene.
However most of the work have concentrated on hand holding objects in general but not
3. Computer Science & Information Technology (CS & IT) 229
specifically sharp and harmful objects like knife. We intend to design a model which identifies
such cases and also notify in case of any threat.
3. METHODOLOGY
The block diagram of proposed suspicious activity detection framework is shown in Fig 1. The
proposed framework consists of two main components: 1. (FBD) Framing and Blob detection
(Input video processing) 2. (HON) Human tracking, Object (like knife) identification and
notification (query Image processing) stage.
Figure 1. Block Diagram of complete algorithm
In the Framing and Blob detection stage as shown in Phase 1 of block diagram in Fig. x, Frames
are extracted from the input surveillance video and compared with respect to the reference
background image. Also the blob detection and analysis is done to calculate the number of blobs
(i.e., human being in concern). If the number of blobs are greater than one then the program
enters the next section i.e., Phase 2 of the block diagram shown in fig. x, otherwise the execution
flow comes to an end for the particular frame and continued with next consecutive frame in the
video.
In the Human tracking, Object (knife) identification and notification stage, edge detection helps
in observing the characteristics of human blob present in the scene. The frame is zoomed-in
concentrating the blobs by cropping the frame into certain dimension and is further divided into
‘x’ regions (and stored in the current working directory). Every block is further processed for
accuracy.
4. 230 Computer Science & Information Technology (CS & IT)
Object detection returns positive result if the three conditions are satisfied; 1.Hand mould and
gesture verification 2.Presence of Sharp object affirmation and 3. Link between the hand and
object to meet the desired soleness. If the scenario is validated to be insecure, the program flow
reaches the Phase 3 of the block diagram shown in fig. x in which a notification along with
relevant photograph or latest timestamped part of video is sent to the concerned security system
like nearby Police department, guard, NGO or anyone who can take quick action.
3.1 Framing and Blob detection (FBD)
The input video is taken from the surveillance camera with the Camera specification being 2MP.
The video is sliced to form frames and is stored in a directory ‘frames’ within present working
directory. A precise initial frame n1 is taken as a base frame with reference to which the further
analysis of the video is done, it is basically the background image.
The looping construct is used in order to traverse the complete set of frames of the video with
frames where i from 2 to number of frames (n) and each frame is inspected against the base
frame. Each frame undergoes the blob detection step. Blob detection is achieved through typical
background subtraction between the current frame and background base frame. Morphological
operations are performed in order to remove the holes, noise and unneeded information from the
subtracted image indicates the number of individuals in the frames which can be determined by
the area calculation method where in a defined threshold is set for the white fields appearing on
the image to become blobs. If a particular white patch meets the threshold set then that particular
object is considered as blob. If the number of blobs in a frame exceeds 2, then the further
processing is continued. Otherwise the process halts anticipating that no hazard can take place as
there is only one person, hence returns to continue with the next frame in the sequence of the
video consecutively. The algorithm is efficiently implemented in order to optimize the resource
usage.
3.2 Human tracking, Object (knife) identification and notification (HON)
Edge detection using Sobel's algorithm helps in human being tracking and systematic user
interface design. The blobs are cropped to ignore the unnecessary processing of complete frame.
The cropping is done using a set of if-else-if ladder along with the decision making factors being
brute force length-width ratio calculation having known the centroid of the blob, originate pixel
and area. The cropped blob is stored in the current working directory of the running program.
The blob concentrated image is further divided into multiple regions to achieve high accuracy and
is accessed from the same PWD (present working directory). The cropped images are further
taken for analysis.
The next step takes the image parts for processing and checks to see if these 3 conditions are
contented. Only if the 3 below noted criteria’s are met, the system tends to notify the desired end
user and further action is left to the security system in charge. The conditions are as follows:
1. Hand mould and gesture verification: The hand posture is evaluated by matching with the
data base images in the repository in complete 360 degrees to see if it is of clenched fist
form. This phase concludes that the hand is holding some object.
5. Computer Science & Information Technology (CS & IT) 231
2. Triangular or sharp object detection: The block of cropped image is checked to identify
sharp object present in the frame. This is achieved by performing triangular object
detection test on the frame by applying certain range of threshold condition on the length-
width ratio, type of triangle, orientation of triangle etc. This ensures the presence of sharp
object (knife like object) present in the frame.
3. There should be a link between hand suggested by condition 1 and possessed object
suggested in condition 2. This framework is not concerned about the presence of sharp
object alone, it should be held by any of the person in the frame. This can be verified by
the logic with soleness check of the object in the image, i.e., there should be a single
speck in the binary frame to confirm that the object x is possessed by the person only.
If all the conditions are satisfied and the flow of execution reaches till the end overcoming every
decision making construct, then it can be concluded that there is presence of danger in the
scenario and hence the function for notification is invoked by the main program. As the
considered security topic is sensitive, the framework defined also needs human intervention for
exact decisions. But human resource is well utilized by having less interposing work. Rather is
required to handle only when the system sends the notification.
Similarly all the frames in the captured video are analysed to detect suspicious activity and
evaluated for the secureness of the real time scenario.
3.3 Workflow
1. Capturing input video and slicing into frames by various Matlab inbuilt functions such as
NumberOfFrames, resd(VideoObj, frameno), imwrite() etc. Identifying reference
background frame.
2. Traversing each frame of the video and analyzing with respect to the considered
reference frame. If there is danger identified, then it is notified and paused for the
response.
3. Adaptive background subtraction and noise removal by Image filtering methods available
in Matlab and other predefined filters.
4. Blob detection and counting by Connected Area Component labeling technique. Break
and start processing next frame if less than two people or blobs. Continue if more than 2
blobs detected.
5. Edge detection by means of Sobel edge detection technique for enhanced user interaction
and human tracking.
6. Notifying through the relevant means if all of the conditions are satisfied: hand mould is
matched, triangle like sharp object present in the frame and link existing between hand
and object.
6. 232 Computer Science & Information Technology (CS & IT)
4. EXPERIMENTAL RESULT AND ANALYSIS
The proposed suspicious activity detection system is evaluated on real time recorded data set for
validation of framework’s procedural tasks. The video database includes 10 hours of video
covering a wide variety of content. The format of reference video clips is 1280*1024 pixels and
30 frames/ sec. In our experiments, video clips that are taken as input are selected from reference
dataset that are captured in 5-6 distinct locations.
The experiment conducted in three different environment as shown in Fig. 2 is taken for analysis.
The first scenario is a well-lit classroom with benches, fans, curtains etc. as shown in fig. 2(a).
The second scenario as shown in Fig 2(b) witnesses the experiment conducted in a university
computer laboratory. The third case is a moderately-lit shuttered indoor space as shown in fig. 2
(c). Video clips shot in all the three locations is 30 to 45 seconds long per instance.
Figure 2. Experimental Input locations
The possible threat situation for the above three cases is shown in Fig. 3 First image depicts a
scenario in which a person is trying to attack another person reading a book in a class, with a
knife behind her back Fig 3(a). In the second image Fig 3(b) a person is pointing a knife towards
the other which accounts for a threat situation. Fig 3(c) shows a person holding a knife in a
shuttered indoor space, very close to another individual which might be a suspicious situation.
Figure 3. Threat Scenarios and their respective notifications
7. Computer Science & Information Technology (CS & IT) 233
The possible threat in all the above three cases are notified by a pop up window shown in fig 3(d,
e, f).
The entire process is done in sequence of steps. For the input image Fig 4(a) the different stages
of the experiment is traced by the subsequent images in Fig 4. Fig 4 (b) shows the blob detection
phase of the experiment where the two individuals are identified as two separate blobs. 4(b) is
obtained by subtracting the Fig 4(a) with the reference background image with appropriate filters.
The edge detection stage is shown by Fig 4(c) which is done using Sobel edge detection method
with a defined threshold. The fourth image Fig 4(d) constantly tracks the foreground image in the
video sequence. The tracking also helps in efficient implementation of user interface. For the
danger notification which is the final step of the experiment, a pop-up window is displayed as
shown in fig 4(e).
Figure 4. Different Stages of the experiment
Apart from all the true positives, the system also shows two false positives and three false
negatives. For the shown fig 5(a) where the person is pointing a finger in the shape of a knife to a
person in the vicinity gives a danger notification irrespective of the absence of the knife. The
Hand shape is mistakenly seen as a knife by the system. In the second image shown in Fig 5(b)
where the person’s fist is falsely matched with the database image giving a false positive
notification.
Figure 5. False Positives
The false negative cases is shown in Fig 6. In the first image Fig 6(a), no message is notified
despite the presence of knife in one’s hand. This is because the knife is being held in a different
fashion for which system is not trained. Fig 6(b) also fails to notify any threat as the light
intensity has caused shining glare on the knife which makes it undetected. In the third image Fig
2.5(c), the two blobs are overlapped along with the blob for the knife which makes it difficult for
the system to detect.
8. 234 Computer Science & Information Technology (CS & IT)
Figure 6. False Negatives
The system is evaluated by a precision and recall metric for the collected dataset from 3 different
locations as mentioned in Fig 2.1. The particular rows represent the cases considered in Fig 2.2. It
includes True positives, True negatives, false positives, false negatives count for the input video
with 100 frames. Also Precision and Recall values are calculated. It is observed that the precision
values decreases and recall increases with the subsequent test cases.
Table 1. PR Table of analysis result
5. CONCLUSION AND FUTURE WORK
This paper proposes a possible technique for Suspicious Activity Detection in indoor places to
enhance the security system by improving the functionality of Surveillance camera to an
intelligent device that is capable of detecting and notifying danger. An observer or the guard is
relieved from the burden of continuous monitoring, may be physically or virtually watching
enormous amount of video sequences captured by multiple Web cameras. Instead, intervention is
serviced when the notification is sent. Due to its cost effectiveness, simple installation, scalability
to different video resolutions, and once in a lifetime initialization, this is the feasible and
practical solution to deploy in real scenarios.
The algorithm works efficiently in bright areas with 73 % accuracy, whereas functions
moderately in less intensified areas with 67 % accuracy when experimented against the real time
videos captured from distinct places.
Our goal is to rigorously continue to improve our detection and notifying system by adding
various other features, such as implementation to detect other harmful activities like striking
down, hitting, snatching the belongings and other physical abuse activities by supporting
additional hand and object poses, expanding the dataset, experimenting in distinct possible
locations, introducing machine learning techniques by using priori incidents knowledge with the
focused concern of maintaining societal security. In the future, we expect promising results on
9. Computer Science & Information Technology (CS & IT) 235
location specific distributional implementation with flattering performance. There is certainly
room to improve the accuracy of the system to reduce the false positives and true negatives and
to investigate on the category of activity and level of impact.
We believe, that the most rewarding impact is foreseen in security security however, the accuracy
of the system needs to be enhanced for increased trustworthiness.
ACKNOWLEDGEMENT
We would like to thank Dr. Roopalakshmi, RVCE, and Bangalore for valuable discussions and
many students of the University for creating vast test videos.
In addition, we would like to thank the anonymous reviewers for their valuable comments and
suggestions that have helped to significantly improve the quality of this manuscript.
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AUTHORS
Nithya Shree R.
Students of R.V College of Engineering, Bangalore, India. I am interested in
introducing technological enhanced solution for real time problems.
Rajeshwari Sah
Student of R.V College of Engineering, Bangalore, India. Most of my work are in
the field of computer vision, image processing and machine learning domain
Shreyank N Gowda
Student of R.V College of Engineering, Bangalore, India. I am interested in the
field of image processing, virtualization and cloud computing domains.