Computer Science ›› 2021, Vol. 48 ›› Issue (7): 199-205.doi: 10.11896/jsjkx.200800146

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining

HOU Chun-ping, ZHAO Chun-yue, WANG Zhi-peng   

  1. School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China
  • Received:2020-08-22 Revised:2020-10-24 Online:2021-07-15 Published:2021-07-02
  • About author:HOU Chun-ping,born in 1957,Ph.D,professor,Ph.D supervisor.Her main research interests include wireless communications and 3D imaging.(hcp@tju.edu.cn)
    WANG Zhi-peng,born in 1992,Ph.D.His main research intersets include video and image anomly detection.
  • Supported by:
    projects of International Cooperation and Exchanges NFSC(61520106002) and National Natural Science Foundation of China(61731003).

Abstract: Video anomaly detection algorithm is one of the hot issues in the field of video processing,and it is used to detect whether an abnormal event is contained in the video.However,since abnormal samples are not involved in the training process,and there is a certain degree of similarity between abnormal samples and normal samples,it is difficult to design an abnormal detection model with discrimination.In order to solve the above problems,firstly,this paper proposes a feature selection method based on similarity preservation and sample recovery.This method can retain the similarity of normal samples,and then learn features that can accurately describe normal events.Secondly,it formalizes the abnormal event detection as classification problem,and proposes a self-feedback optimal subclass mining method to find optimal classifier.The sample will be labeled as anomaly if all classifiers label it as anomaly.Extensive experiments on public video surveillance datasets (i.e.Avenue Dataset and UCSD Ped2 Dataset) demonstrate that the proposed abnormal event detection method can achieve good results.

Key words: Feature selection, One-class SVM, Optimal subclass mining, Self-feedback, Video abnormal event detection

CLC Number: 

  • TN911.73
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