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Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos

文献类型：期刊论文

作者Wang, Jinqiao1; Fu, Wei2; Lu, Hanqing1; Ma, Songde1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2014-12-01
卷号23期号:12页码:5198-5208
关键词Dynamic scene analysis sparse coding topic model
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]CLASSIFICATION
收录类别SCI
语种英语
WOS记录号WOS:000344466600003
源URL[http://ir.ia.ac.cn/handle/173211/3342]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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