Background subtraction based on modified online ro

Authors: Guang Han Jinkuan Wang Xi Cai

Publish Date: 2016/07/06

Volume: 8, Issue: 6, Pages: 1839-1852

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Abstract

In video surveillance camera jitter occurs frequently and poses a great challenge to foreground detection To overcome this challenge without any additional antijitter 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 priorinformationbased 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 pixelbased background models our method utilizes the lowdimensional 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

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