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Drastic Anomaly Detection in Video Using Motion Direction Statistics
Chang LIU Guijin WANG Wenxin NING Xinggang LIN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2011/08/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
visual surveillance, anomaly detection, motion vector, one-class SVM, PCA,
Full Text: PDF(908.2KB)>>
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.