Sparse representation for robust abnormality detection in crowded scenes

	Sparse representation for robust abnormality detection in crowded scenes
	Zhu, Xiaobin 1,2; Liu, Jing2 ; Wang, Jinqiao2 ; Li, Changsheng 3; Lu, Hanqing2
发表期刊	PATTERN RECOGNITION
	2014-05-01
卷号	47 期号:5 页码:1791-1799
文章类型	Article
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.
关键词	Nonnegative Matrix Factorization Crowded Scene Abnormality Detection Sparse Coding Earth Mover's Distance Wavelet Emd
WOS标题词	Science & Technology ; Technology
关键词[WOS]	NONNEGATIVE MATRIX FACTORIZATION ; EARTH-MOVERS-DISTANCE ; FLOW
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000331667400001
引用统计	被引频次：84[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3370
专题	紫东太初大模型研究中心_图像与视频分析
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
第一作者单位	模式识别国家重点实验室
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.

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