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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/97001


    Title: Abnormal Event Detection Using HOSF
    Authors: Yen, Shwu-Huey;Wang, Chun-Hui
    Contributors: 淡江大學資訊工程學系
    Keywords: normality;crowd;social force (SF);histogram of oriented social force (HOSF);z-value
    Date: 2013-12-16
    Issue Date: 2014-03-14 16:25:32 (UTC+8)
    Abstract: In this paper a simple and effective crowd behavior normality method is proposed. We use the histogram of oriented social force (HOSF) as the feature vector to encode the observed events of a surveillance video. A dictionary of codewords is trained to include typical HOSFs. To detect whether an event is normal is accomplished by comparing how similar to the closest codeword via z-value. The proposed method includes the following characteristic: (1) the training is automatic without human labeling; (2) instead of object tracking, the method integrates particles and social force as feature descriptors; (3) z-score is used in measuring the normality of events. The method is testified by the UMN dataset with promising results.
    Relation: Proceedings of the International Conference on IT Convergence and Security (ICITCS 2013), 4p.
    Appears in Collections:[資訊工程學系暨研究所] 會議論文

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