Drastic Anomaly Detection in Video Using Motion Direction Statistics

• LIU Chang

  Department of Electronic Engineering, Tsinghua University

• WANG Guijin

  Department of Electronic Engineering, Tsinghua University

• NING Wenxin

  Department of Electronic Engineering, Tsinghua University

• LIN Xinggang

  Department of Electronic Engineering, Tsinghua University

抄録

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.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E94-D (8), 1700-1707, 2011

  一般社団法人 電子情報通信学会

キーワード

• visual surveillance
• anomaly detection
• motion vector
• one-class SVM
• PCA

詳細情報

• CRID: 1390001204379158528
• NII論文ID: 10030192734
• NII書誌ID: AA10826272
• DOI: 10.1587/transinf.e94.d.1700
• ISSN: 17451361
• ISSN: 09168532
• Web Site: http://www.jstage.jst.go.jp/article/transinf/E94.D/8/E94.D_8_1700/_pdf
• 本文言語コード: en
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