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
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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.
- Avoin saatavuus