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题名: Abnormal event detection in crowded scenes using sparse representation
作者: Cong Y(丛杨) ; Yuan JS(袁浚菘) ; Liu J(刘霁)
作者部门: 机器人学研究室
关键词: Convex optimization ; Security systems
刊名: Pattern Recognition
ISSN号: 0031-3203
出版日期: 2013
卷号: 46, 期号:7, 页码:1851-1864
收录类别: SCI ; EI
产权排序: 1
摘要: 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.
语种: 英语
WOS记录号: WOS:000317886600012
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]: IMAGES
研究领域[WOS]: Computer Science ; Engineering
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/10626
Appears in Collections:机器人学研究室_期刊论文

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