Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition

doi:10.1109/TIP.2016.2627803

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition
	Hu, Wenrui; Yang, Yehui; Zhang, Wensheng; Xie, Yuan
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2017-02-01
卷号	26 期号:2 页码:724-737
关键词	Moving Object Detection Tensor Nuclear Norm Tensor Total Variation Space-time Visual Saliency
DOI	10.1109/TIP.2016.2627803
文献子类	Article
英文摘要	In this paper, we propose a new low-rank and sparse representation model for moving object detection. The model preserves the natural space-time structure of video sequences by representing them as three-way tensors. Then, it operates the low-rank background and sparse foreground decomposition in the tensor framework. On the one hand, we use the tensor nuclear norm to exploit the spatio-temporal redundancy of background based on the circulant algebra. On the other, we use the new designed saliently fused-sparse regularizer (SFS) to adaptively constrain the foreground with spatio-temporal smoothness. To refine the existing foreground smooth regularizers, the SFS incorporates the local spatio-temporal geometric structure information into the tensor total variation by using the 3D locally adaptive regression kernel (3D-LARK). What is more, the SFS further uses the 3D-LARK to compute the space-time motion saliency of foreground, which is combined with the l(1) norm and improves the robustness of foreground extraction. Finally, we solve the proposed model with globally optimal guarantee. Extensive experiments on challenging well-known data sets demonstrate that our method significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex scenarios.
WOS关键词	BACKGROUND SUBTRACTION ; VISUAL SURVEILLANCE ; REGULARIZATION ; FRAMEWORK ; RECOVERY ; ROBUST ; IMAGE
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000404773100010
资助机构	National Natural Science Foundation of China(61402480 ; 61432008 ; 61472423 ; 61502495 ; 61532006)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15244]
专题	精密感知与控制研究中心_人工智能与机器学习
作者单位	Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hu, Wenrui,Yang, Yehui,Zhang, Wensheng,et al. Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(2):724-737.
APA	Hu, Wenrui,Yang, Yehui,Zhang, Wensheng,&Xie, Yuan.(2017).Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(2),724-737.
MLA	Hu, Wenrui,et al."Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.2(2017):724-737.