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Abnormal event detection in crowded scenes using sparse representation
Cong Y(丛杨)1; Yuan JS(袁浚菘)2; Liu J(刘霁)3
Department机器人学研究室
Source PublicationPattern Recognition
ISSN0031-3203
2013
Volume46Issue:7Pages:1851-1864
Indexed BySCI ; EI
EI Accession number20131316148901
WOS IDWOS:000317886600012
Contribution Rank1
KeywordConvex Optimization Security Systems
Abstract

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O( k2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.

Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordImages
WOS Research AreaComputer Science ; Engineering
Citation statistics
Cited Times:124[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10626
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.Department of EEE, Nanyang Technological University, Singapore, Singapore
3.Department of Computer Sciences, University of Wisconsin-Madison, United States
Recommended Citation
GB/T 7714
Cong Y,Yuan JS,Liu J. Abnormal event detection in crowded scenes using sparse representation[J]. Pattern Recognition,2013,46(7):1851-1864.
APA Cong Y,Yuan JS,&Liu J.(2013).Abnormal event detection in crowded scenes using sparse representation.Pattern Recognition,46(7),1851-1864.
MLA Cong Y,et al."Abnormal event detection in crowded scenes using sparse representation".Pattern Recognition 46.7(2013):1851-1864.
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