Applied-Information Technology in Abnormal Detection for Surveillance Systems

2014 ◽  
Vol 1046 ◽  
pp. 266-269
Author(s):  
Feng Xu
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Detection System ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Criminal Cases ◽  
Abnormal Detection ◽  
Abnormal Behavior Detection ◽  
Artificial Intelligence Technology ◽  
Function Module

In recent years, video surveillance has become more and more important for enhanced security and it is indispensable technology for fighting against all types of crime with the construction of sky-net in China. Abnormal detection is the focus of intelligent video surveillance and the information of abnormal behavior can be used in the investigation of criminal cases, which combines computer vision and artificial intelligence technology and has wide application prospect in public security work. In this paper, first the current research situation of the intelligent surveillance system is introduced. Then the category of abnormal behavior detection is expounded. Finally the function module of abnormal detection system is designed and the key technology of moving target detection, target tracking and abnormality judgment is discussed in view of the actual situation of surveillance system in criminal cases.

scholarly journals Abnormal Human Behavior Detection in Videos: A Review

2021 ◽  
Vol 50 (3) ◽  
pp. 522-545
Author(s):  
Huiyu Mu ◽  
Ruizhi Sun ◽  
Gang Yuan ◽  
Yun Wang
Keyword(s):  
Video Surveillance ◽  
Human Behavior ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Behavior Detection ◽  
Event Modeling ◽  
Semantic Inference ◽  
Abnormal Behavior Detection ◽  
Private And Public ◽  
New Perspective

Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

scholarly journals An Automated Surveillance System Based on Multi-Processor and GPU Architecture

2017 ◽  
Vol 7 (6) ◽  
pp. 2319-2323
Author(s):  
M. B. Ayed ◽  
S. Elkosantini ◽  
M. Abid
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Human Vision ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Human Errors ◽  
Complex Procedure ◽  
Automated Surveillance ◽  
Definition Of ◽  
Gpu Architecture

Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelerate the system and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section illustrates the results with comparison of some of the most recent approaches.

Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras

2020 ◽  
Vol 27 (4) ◽  
pp. 373-387 ◽  
Author(s):  
Jesus Benito-Picazo ◽  
Enrique Domínguez ◽  
Esteban J. Palomo ◽  
Ezequiel López-Rubio
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Low Cost ◽  
Abnormal Behavior ◽  
Video Frame ◽  
Raspberry Pi ◽  
Video Surveillance System ◽  
Surveillance Systems

The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented on low cost devices, due to the excessive computational load generated by the examination of multiple image windows. This work presents a video surveillance system aimed to detect moving objects with abnormal behavior for a panoramic 360∘ surveillance camera. The block of the video frame to be analyzed is determined on the basis of a probabilistic mixture distribution comprised by two mixture components. The first component is a uniform distribution, which is in charge of a blind window selection, while the second component is a mixture of kernel distributions. The kernel distributions generate windows within the video frame in the vicinity of the areas where anomalies were previously found. This contributes to obtain candidate windows for analysis which are close to the most relevant regions of the video frame, according to the past recorded activity. A Raspberry Pi microcontroller based board is employed to implement the system. This enables the design and implementation of a system with a low cost, which is nevertheless capable of performing the video analysis with a high video frame processing rate.

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

scholarly journals Abnormal Human Activity Detection using Unsupervised Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 3949-3953
Keyword(s):  
Machine Learning ◽  
Human Activity ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Abnormal Behavior ◽  
Activity Detection ◽  
Research Attention ◽  
Learning Techniques ◽  
Abnormal Behavior Detection ◽  
Human Activity Detection

Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people

Video Surveillance

2015 ◽  
pp. 69-92 ◽  
Author(s):  
Qasim Mahmood Rajpoot ◽  
Christian Damsgaard Jensen
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Negative Impact ◽  
Developed Countries ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Security Threats ◽  
Privacy Requirements ◽  
Privacy Issues ◽  
Legal Infrastructure

Pervasive usage of video surveillance is rapidly increasing in developed countries. Continuous security threats to public safety demand use of such systems. Contemporary video surveillance systems offer advanced functionalities which threaten the privacy of those recorded in the video. There is a need to balance the usage of video surveillance against its negative impact on privacy. This chapter aims to highlight the privacy issues in video surveillance and provides a model to help identify the privacy requirements in a video surveillance system. The authors make a step in the direction of investigating the existing legal infrastructure for ensuring privacy in video surveillance and suggest guidelines in order to help those who want to deploy video surveillance while least compromising the privacy of people and complying with legal infrastructure.

An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony

2016 ◽  
Vol 12 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Javidan
Keyword(s):  
Video Surveillance ◽  
Traffic Engineering ◽  
Surveillance System ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Video Data ◽  
Surveillance Systems ◽  
Bee Colony

In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

scholarly journals A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

Based Embedded System Applications in Intelligent Home Remote Monitoring

2014 ◽  
Vol 602-605 ◽  
pp. 2317-2320
Author(s):  
Yang Li ◽  
Qing Hong Wu ◽  
Xue Xiao
Keyword(s):  
Embedded System ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Video Capture ◽  
Home Video ◽  
Intelligent Home ◽  
Front End ◽  
Usb Camera

With the continuous improvement of security awareness, home security has become the focus of attention. The actual demand for home video surveillance system, designed a cheap, practical, small size and low power consumption of video surveillance systems, this paper uses microprocessor S3C2440 ARM9 core as the core hardware control, embedded Linux operating system with software the control core, and cheap, generic USB camera video capture device as a front end to complete the design of a home video surveillance system.

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