A Novel Detection Framework for Detecting Abnormal Human Behavior

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.

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Abnormal Human Behavior Detection in Videos: A Review

Information Technology And Control ◽

10.5755/j01.itc.50.3.27864 ◽

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.

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Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Mobile Information Systems ◽

10.1155/2021/9977736 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Ni Hong ◽

Xuefeng Wang ◽

Zhonghua Wang

Keyword(s):

Moral Education ◽

Access Network ◽

Colleges And Universities ◽

Abnormal Behavior ◽

Detection Methods ◽

Network Behavior ◽

Behavior Detection ◽

Detection Model ◽

Abnormal Behavior Detection ◽

Effectiveness And Efficiency

In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities. Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior. In order to prove the effectiveness and efficiency of the proposed algorithm, the algorithm is compared with the other two network abnormal behavior detection methods, and the results prove that the proposed method can effectively detect the abnormal access behavior in ideological and political MOOCs.

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Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽

10.3390/electronics10020197 ◽

2021 ◽

Vol 10 (2) ◽

pp. 197

Author(s):

Meng-ting Fang ◽

Zhong-ju Chen ◽

Krzysztof Przystupa ◽

Tao Li ◽

Michal Majka ◽

...

Keyword(s):

Detection Method ◽

Abnormal Behavior ◽

Detection Accuracy ◽

Average Precision ◽

Research Results ◽

Behavior Detection ◽

Abnormal Behavior Detection ◽

The Mean ◽

Examination Room ◽

Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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