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Sparse representation for robust abnormality detection in crowded scenes

文献类型：期刊论文

作者Zhu, Xiaobin1,2; Liu, Jing2; Wang, Jinqiao2; Li, Changsheng3; Lu, Hanqing2
刊名PATTERN RECOGNITION
出版日期2014-05-01
卷号47期号:5页码:1791-1799
关键词Nonnegative matrix factorization Crowded scene Abnormality detection Sparse coding Earth mover's distance Wavelet EMD
英文摘要In crowded scenes, the extracted low-level features, such as optical flow or spatio-temporal interest point, are inevitably noisy and uncertainty. In this paper, we propose a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty. The abnormality of query sample is decided by the sparse reconstruction cost from an atomically learned event dictionary, which forms a sparse coding bases. In our algorithm, we formulate the task of dictionary learning as a non-negative matrix factorization (NMF) problem with a sparsity constraint. We take the robust Earth Mover's Distance (EMD), instead of traditional Euclidean distance, as distance metric reconstruction cost function. To reduce the computation complexity of EMD, an approximate EMD, namely wavelet EMD, is introduced and well combined into our approach, without losing performance. In addition, the combination of wavelet EMD with our approach guarantees the convexity of optimization in dictionary learning. To handle both local abnormality detection (LAD) and global abnormality detection, we adopt two different types of spatio-temporal basis. Experiments conducted on four public available datasets demonstrate the promising performance of our work against the state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.
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]NONNEGATIVE MATRIX FACTORIZATION ; EARTH-MOVERS-DISTANCE ; FLOW
收录类别SCI
语种英语
WOS记录号WOS:000331667400001
源URL[http://ir.ia.ac.cn/handle/173211/3370]  
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.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.IBM Res China, Beijing 100193, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Xiaobin,Liu, Jing,Wang, Jinqiao,et al. Sparse representation for robust abnormality detection in crowded scenes[J]. PATTERN RECOGNITION,2014,47(5):1791-1799.
APA Zhu, Xiaobin,Liu, Jing,Wang, Jinqiao,Li, Changsheng,&Lu, Hanqing.(2014).Sparse representation for robust abnormality detection in crowded scenes.PATTERN RECOGNITION,47(5),1791-1799.
MLA Zhu, Xiaobin,et al."Sparse representation for robust abnormality detection in crowded scenes".PATTERN RECOGNITION 47.5(2014):1791-1799.

入库方式： OAI收割

来源：自动化研究所

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