Data Separation of L1-minimization for Real-time Motion Detection

Yu Liu, Huaxin Xiao, Zheng Zhang, Wei Xu, Maojun Zhang and Jianguo Zhang

Abstract

The L1-minimization used to seek the sparse solution restricts the applicability of compressive sensing theory. This paper proposes a data separation algorithm with computationally efficient strategies to achieve real-time processing for sparse model based motion detection. We regard the traditional pursuit algorithms as a pre-process step that converts the iterative optimization into linear addition and multiplication operations. A novel motion detection method is implemented to compare the difference between the current frame and the background model in terms of sparse coefficients. The influence of dynamic texture or statistical noise diminishes after the process of sparse projection; thus, enhancing the robustness of the implementation. Results of the qualitative and quantitative evaluations demonstrate the higher efficiency and effectiveness of the proposed approach compared with those of other competing methods.

Session

Poster 1

Files

Extended Abstract (PDF, 1820K)
Paper (PDF, 2M)

DOI

10.5244/C.29.75
https://dx.doi.org/10.5244/C.29.75

Citation

Bibtex

@inproceedings{BMVC2015_75,
	title={Data Separation of  L1-minimization for Real-time Motion Detection},
	author={Yu Liu and Huaxin Xiao and Zheng Zhang and Wei Xu and Maojun Zhang and Jianguo Zhang},
	year={2015},
	month={September},
	pages={75.1-75.12},
	articleno={75},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.75},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.75}
}