Drastic Anomaly Detection in Video Using Motion Direction Statistics

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著者

    • LIU Chang
    • the Department of Electronic Engineering, Tsinghua University
    • WANG Guijin
    • the Department of Electronic Engineering, Tsinghua University
    • NING Wenxin
    • the Department of Electronic Engineering, Tsinghua University
    • LIN Xinggang
    • the 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 94(8), 1700-1707, 2011-08-01

    The Institute of Electronics, Information and Communication Engineers

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各種コード

  • NII論文ID(NAID)
    10030192734
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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