Research of Elderly People's Abnormal Behavior Detection System by Floor Laying Pressure Sensor

2017 ◽  
Vol 2017 (0) ◽  
pp. 2A2-I05
Author(s):  
Hiroaki ONO ◽  
Syumpei EGUCHI ◽  
Hikaru TAKEDA ◽  
Hirokazu SEKI
Keyword(s):  
Pressure Sensor ◽  
Detection System ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
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.

scholarly journals A Novel Detection Framework for Detecting Abnormal Human Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chengfei Wu ◽  
Zixuan Cheng
Keyword(s):  
Human Behavior ◽  
Public Safety ◽  
Space Time ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Time Block ◽  
Safety Issues ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
Abnormal Behaviors

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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