Background subtraction via coherent trajectory decomposition

Zhixiang Ren, Liang-Tien Chia, Deepu Rajan, Shenghua Gao

Abstract: Background subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we propose an efficient background subtraction method based on coherent trajectory decomposition. We assume that the trajectories from background lie in a low-rank subspace, and foreground trajectories are sparse outliers in this background subspace. Meanwhile, the Markov Random Field (MRF) is used to encode the spatial coherency and trajectory consistency. With the low-rank decomposition and the MRF, our method can better handle videos with moving camera and obtain coherent foreground. Experimental results on a video dataset show our method achieves very competitive performance.