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	Anomaly Detection in Crowds Using a Space MRF with Incremental Updates
	Nannan Li; Dan Xu; Xinyu Wu; Guoyuan Liang
	2013
会议名称	5th International Conference on Digital Image Processing, ICDIP 2013
会议地点	Beijing, China
英文摘要	In this paper, we propose a space Markov Random Field (MRF) model to detect abnormal activities in crowded scenes. The nodes of MRF graph consist of monitors evenly spread on the image, and neighboring nodes in space are associated with links. The normal patterns of activity at each node are learnt by constructing a Gaussian Mixture Model (GMM) upon optical flow locally, while correlation between adjacent nodes is represented by building a single Gaussian model upon inner product of histogram vectors of optical flow observed from a region centered at each node respectively. For any optical flow patterns detected in test video clips, we use the learnt model and MRF graph to calculate an energy value for each local node, and determine whether the behavior pattern of the node is normal or abnormal by comparing the value with a threshold. Further, we apply a method similar to updating of GMM for background subtraction to incrementally update the current model to adapt for visual context changes over a long period of time. Experiments on the published UCSD anomaly datasets Ped1 and Ped2 show the effectiveness of our method. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/4609]
专题	深圳先进技术研究院_集成所
作者单位	2013
推荐引用方式 GB/T 7714	Nannan Li,Dan Xu,Xinyu Wu,et al. Anomaly Detection in Crowds Using a Space MRF with Incremental Updates[C]. 见:5th International Conference on Digital Image Processing, ICDIP 2013. Beijing, China.

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