基于自反馈最优子类挖掘的视频异常检测算法

doi:10.11896/jsjkx.200800146

Abstract

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

HOU Chun-ping, ZHAO Chun-yue, WANG Zhi-peng. Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining[J].Computer Science, 2021, 48(7): 199-205.

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

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