Background subtraction based on modified online robust principal component analysis

摘要

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.