Drastic Anomaly Detection in Video Using Motion Direction Statistics

Chang LIU  Guijin WANG  Wenxin NING  Xinggang LIN  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.8   pp.1700-1707
Publication Date: 2011/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1700
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
visual surveillance,  anomaly detection,  motion vector,  one-class SVM,  PCA,  

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A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts: (a) a dense motion field and motion statistics method, (b) motion directional PCA for feature dimensionality reduction, (c) an improved one-class SVM for one-class classification. Experiments demonstrate the effectiveness of the proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Our scheme works well in complicated situations that common tracking or detection modules cannot handle.