Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos

	Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos
	Wang, Jinqiao1 ; Fu, Wei 2; Lu, Hanqing1 ; Ma, Songde1
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2014-12-01
卷号	23 期号:12 页码:5198-5208
文章类型	Article
摘要	Dynamic scene analysis has become a popular research area especially in video surveillance. The goal of this paper is to mine semantic motion patterns and detect abnormalities deviating from normal ones occurring in complex dynamic scenarios. To address this problem, we propose a data-driven and scene-independent approach, namely, Bilayer sparse topic model (BiSTM), where a given surveillance video is represented by a word-document hierarchical generative process. In this BiSTM, motion patterns are treated as latent topics sparsely distributed over low-level motion vectors, whereas a video clip can be sparsely reconstructed by a mixture of topics (motion pattern). In addition to capture the characteristic of extreme imbalance between numerous typical normal activities and few rare abnormalities in surveillance video data, a one-class constraint is directly imposed on the distribution of documents as a discriminant priori. By jointly learning topics and one-class document representation within a discriminative framework, the topic (pattern) space is more specific and explicit. An effective alternative iteration algorithm is presented for the model learning. Experimental results and comparisons on various public data sets demonstrate the promise of the proposed approach.
关键词	Dynamic Scene Analysis Sparse Coding Topic Model
WOS标题词	Science & Technology ; Technology
关键词[WOS]	CLASSIFICATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000344466600003
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3342
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Elect Technol Grp Corp, Res Inst 54, Shijiazhuang 050081, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wang, Jinqiao,Fu, Wei,Lu, Hanqing,et al. Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(12):5198-5208.
APA	Wang, Jinqiao,Fu, Wei,Lu, Hanqing,&Ma, Songde.(2014).Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(12),5198-5208.
MLA	Wang, Jinqiao,et al."Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.12(2014):5198-5208.

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