Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 8, Issue 6, Pages 1839-1852Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-016-0562-7
Keywords
Background subtraction; Online robust principal component analysis; Camera jitter; Adaptive weighting parameter; Prior information
Categories
Funding
- National Natural Science Foundation of China [61374097]
- Fundamental Research Funds for the Central Universities [N130423006]
- Foundation of Northeastern University at Qinhuangdao [XNK201403]
Ask authors/readers for more resources
In video surveillance, camera jitter occurs frequently and poses a great challenge to foreground detection. To overcome this challenge without any additional anti-jitter preprocessing, we propose a background subtraction method based on modified online robust principal component analysis (ORPCA). We modify the original ORPCA algorithm by introducing a prior-information-based adaptive weighting parameter to make our method adapt to variation of sparsity of foreground objects among frames, which can substantially improve the accuracy of foreground detection. In detail, we utilize sparsity of our foreground detection result of the last frame as the prior information, and adaptively adjust the weighting parameter of the sparse term for the current frame. Moreover, to make the modified ORPCA applicable to foreground detection, we also reduce the dimension of input frames through representing unoverlapped blocks by their median values. Different from recent advanced methods that rely on pixel-based background models, our method utilizes the low-dimensional subspace constructed by backgrounds of previous frames to estimate background of a new input frame, and hence can well handle the camera jitter. Experimental results demonstrate that, our method achieves remarkable results and outperforms several advanced methods in coping with the camera jitter.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available