Background subtraction for surveillance videos with camera jitter

Abstract

Camera jitter occurs frequently in outdoor scenes and poses a great challenge to foreground detection. To meet this challenge, we propose a background subtraction method based on online robust principal component analysis (OR-PCA). We downsample every input frame in a random manner to make the OR-PCA applicable to video processing. Different from most background subtraction methods which 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. We find that the resulting sparse matrix contains not only the foreground objects but also some sparse noise, and then eliminate the sparse noise to improve the precision of our method. Experimental results demonstrate that, our method achieves remarkable results and outperforms several advanced methods in dealing with the camera jitter.