Crowd abnormal event detection based on motion entropy and dual support vector data description

Xinlu Zong, Zhen Chen () and Lu Zhang ()
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Xinlu Zong: School of Computer Science, Hubei University of Technology, Wuhan 430068, P. R. China
Zhen Chen: School of Computer Science, Hubei University of Technology, Wuhan 430068, P. R. China
Lu Zhang: School of Computer Science, Hubei University of Technology, Wuhan 430068, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 07, 1-18

Abstract: Abnormal event detection is a popular research direction in the field of intelligent transportation and public safety. The features that characterize abnormal events are extracted from given video sequence through computer vision technology. Then the abnormal events in the video are automatically detected through the classification model. In order to describe the motion characteristics of events more accurately, a new feature based on motion entropy is proposed in this paper. The entropy value of motion pixels in the video frame is calculated as the input feature of the classification model. Motion entropy is suitable to regard as a feature to distinguish normal events from abnormal events due to the big differences between normal and abnormal events. In addition, an abnormal event detection model based on motion entropy and dual support vector data description (ME-DSVDD) is presented to solve the problem of insufficient sample diversity. The standard data set is tested to analyze the performance of the proposed model. The experimental results show that the proposed method can effectively improve the performance of the abnormal event detection model.

Keywords: Crowd abnormal event detection; motion entropy; support vector data description; computer vision (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123500870

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