Temporal-Spatial Coherence Based Abnormal Behavior Detection

被引：0
|
作者：
Sun, Xian [1 ]
Zhu, Songhao [1 ]
Cheng, Yanyun [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210023, Jiangsu, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
Anomaly detection; video block; UCSD dataset; Subway dataset;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To improve the accuracy and speed of the local abnormal detection, a novel method based on Temporal-Spatial Coherence model is proposed. Specifically, the video block is firstly extracted using the gradient histogram and optimized based on the temporal-spatial coherence. Then the normal behavior model and abnormal behavior model is learned via the tensor voting algorithm and the temporal-spatial coherence respectively. Finally, abnormal behavior is detected and labeled. The experiments conducted on the public UCSD and Subway datasets demonstrate the efficiency of the proposed method for local abnormal behavior detection.
引用
收藏
页码：1997 / 2001
页数：5
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