CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos
Wang, Jinqiao1; Fu, Wei2; Lu, Hanqing1; Ma, Songde1
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2014-12-01
Volume23Issue:12Pages:5198-5208
SubtypeArticle
AbstractDynamic 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.
KeywordDynamic Scene Analysis Sparse Coding Topic Model
WOS HeadingsScience & Technology ; Technology
WOS KeywordCLASSIFICATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000344466600003
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3342
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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