Computer Science ›› 2023, Vol. 50 ›› Issue (5): 146-154.doi: 10.11896/jsjkx.220400227

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Pseudo-abnormal Sample Selection for Video Anomaly Detection

ZHAO Song, FU Hao, WANG Hongxing   

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University), Ministry of Education, Chongqing 400044, China
    School of Big Data & Software Engineering,Chongqing University,Chongqing 400044,China
  • Received:2022-04-22 Revised:2022-09-12 Online:2023-05-15 Published:2023-05-06
  • About author:ZHAO Song,born in 1997,postgra-duate,is a student member of China Computer Federation.His main research interests include computer vision and video anomaly detection.
    WANG Hongxing,born in 1985,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include computer vision and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61976029) and Key Project of Chongqing Technology Innovation and Application Development(cstc2021jscx-gksbX0033).

Abstract: Unsupervised video anomaly detection methods generally use normal video data to train an anomaly detection model through frame reconstruction or frame prediction.However,normal videos usually contain a large number of background frames and similar scenes,which are quite redundant,leading to inefficient modeling for video anomaly detection.To address this issue,this paper proposes a pseudo-abnormal sample selection method,which iteratively selects video frames with high abnormal scores from original videos to build a new concise training pool for video anomaly detection based on future frame prediction.As for the detection model,this paper designs a two-path U-Net architecture,where each path has a different sampling frequency on video frames so that spatial-temporal features of videos can be better extracted and utilized from multiple scales.In the two-path U-Net,each layer shares a memory module to strengthen the impact of typical training data for future frame prediction and video anomaly detection.Experimental evaluation on benchmark video datasets demonstrates the efficiency and effectiveness of the proposed method.

Key words: Video surveillance, Anomaly detection, Sample selection, Memory model, Spatial-Temporal feature

CLC Number: 

  • TP391.4
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