| 標題：Abnormal Event Detection Using HOSF |
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| 學年 | 102 |
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| 學期 | 1 |
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| 發表日期 | 2013/12/16 |
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| 作品名稱 | Abnormal Event Detection Using HOSF |
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| 作品名稱（其他語言） | |
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| 著者 | Yen, Shwu-Huey; Wang, Chun-Hui |
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| 作品所屬單位 | 淡江大學資訊工程學系 |
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| 出版者 | |
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| 會議名稱 | The International Conference on IT Convergence and Security (ICITCS 2013) |
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| 會議地點 | Macau |
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| 摘要 | 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. |
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| 關鍵字 | normality;crowd;social force (SF);histogram of oriented social force (HOSF);z-value |
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| 語言 | 英文 |
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| 收錄於 | |
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| 會議性質 | 國際 |
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| 校內研討會地點 | |
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| 研討會時間 | 20131216~20131218 |
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| 通訊作者 | |
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| 國別 | 澳門 |
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| 公開徵稿 | Y |
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| 出版型式 | 電子版 |
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| 出處 | Proceedings of the International Conference on IT Convergence and Security (ICITCS 2013), 4p. |
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| 相關連結 | |
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| SDGs | |
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