Spatio-temporal Context Analysis within Video Volumes for Anomalous-event Detection and Localization

	Spatio-temporal Context Analysis within Video Volumes for Anomalous-event Detection and Localization
	Nannan Li; Xinyu Wu; Dan Xu; Huiwen Guo; Wei Feng
刊名	Neurocomputing
	2015
英文摘要	In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatio-temporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S0925231214017287
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/6664]
专题	深圳先进技术研究院_集成所
作者单位	Neurocomputing
推荐引用方式 GB/T 7714	Nannan Li,Xinyu Wu,Dan Xu,et al. Spatio-temporal Context Analysis within Video Volumes for Anomalous-event Detection and Localization[J]. Neurocomputing,2015.
APA	Nannan Li,Xinyu Wu,Dan Xu,Huiwen Guo,&Wei Feng.(2015).Spatio-temporal Context Analysis within Video Volumes for Anomalous-event Detection and Localization.Neurocomputing.
MLA	Nannan Li,et al."Spatio-temporal Context Analysis within Video Volumes for Anomalous-event Detection and Localization".Neurocomputing (2015).