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	Multi-Scale Analysis of Contextual Information Within Spatio-Temporal Video Volumes for Anomaly Detection
	Nannan Li; Huiwen Guo; Dan Xu; Xinyu Wu
	2014
会议名称	Image Processing (ICIP), 2014 IEEE International Conference on
会议地点	法国
英文摘要	In this paper, we present a novel approach for video anomaly detection in crowded scenes. The proposed approach detects anomalies based on the contextual information analysis within spatio-temporal video volume. Around each pixel, spatio-temporal volumes are built and clustered to construct the activity pattern codebook. Then, the composition information of the volumes within a large spatiotemporal window is described via a dictionary learned by sparse representation. Furthermore, multi-scale analysis is employed to adapt the size change of abnormal events. Finally, the sparse reconstruction cost is designed to evaluate the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the existing public available anomaly-detection datasets and the performance comparasion with three existing methods validates that the proposed method detects anomalies more accurately.
收录类别	其他
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5661]
专题	深圳先进技术研究院_集成所
作者单位	2014
推荐引用方式 GB/T 7714	Nannan Li,Huiwen Guo,Dan Xu,et al. Multi-Scale Analysis of Contextual Information Within Spatio-Temporal Video Volumes for Anomaly Detection[C]. 见:Image Processing (ICIP), 2014 IEEE International Conference on. 法国.

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