Background subtraction using spatio-temporal group sparsity recovery
Liu, Xin; Yao, Jiawen; Hong, Xiaopeng; Huang, Xiaohua; Zhou, Ziheng; Qi, Chun; Zhao, Guoying (2017-04-25)
Liu, X., Yao, J., Hong, X., Huang, X., Zhou, Z., Qi, C., Zhao, G. (2018) Background Subtraction Using Spatio-Temporal Group Sparsity Recovery. IEEE Transactions on Circuits and Systems for Video Technology, 28 (8), 1737-1751. doi:10.1109/TCSVT.2017.2697972
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https://urn.fi/URN:NBN:fi-fe2018102638831
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Abstract
Background subtraction is a key step in a wide spectrum of video applications, such as object tracking and human behavior analysis. Compressive sensing-based methods, which make little specific assumptions about the background, have recently attracted wide attention in background subtraction. Within the framework of compressive sensing, background subtraction is solved as a decomposition and optimization problem, where the foreground is typically modeled as pixel-wised sparse outliers. However, in real videos, foreground pixels are often not randomly distributed, but instead, group clustered. Moreover, due to costly computational expenses, most compressive sensing-based methods are unable to process frames online. In this paper, we take into account the group properties of foreground signals in both spatial and temporal domains, and propose a greedy pursuit-based method called spatio-temporal group sparsity recovery, which prunes data residues in an iterative process, according to both sparsity and group clustering priors, rather than merely sparsity. Furthermore, a random strategy for background dictionary learning is used to handle complex background variations, while foreground-free training is not required. Finally, we propose a two-pass framework to achieve online processing. The proposed method is validated on multiple challenging video sequences. Experiments demonstrate that our approach effectively works on a wide range of complex scenarios and achieves a state-of-the-art performance with far fewer computations.
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