Multi-component group sparse RPCA model for motion object detection under complex dynamic background

Robust PCA model and its variants are promising tools for motion object detection, which decompose video or image sequences matrix into a low rank background component and a sparse moving object component. Although they can handle static background well, the background motion is usually mixed in the sparse components under the condition of complex dynamic background, such as fountains, ripples, and shaking leaves, etc. Meanwhile, the detected boundaries of foreground objects are usually inaccurate and incomplete. In this paper, a multi-component group sparse RPCA model is proposed to cope with all the difficulties mentioned above. With the aiming to separate foreground motion object from dynamic background, our model represents the observed video or image sequences as three components, i.e., a low-rank static background, a group sparse foreground, and a dynamic background. In order to integrate the object boundary prior, each frame is over-segmented into super-pixels which are taken as the group information to define a group sparse norm. Accordingly, the group sparse norm takes each super-pixels as a whole to measure the sparse foreground. Furthermore, an incoherence term is introduced to enhance the separability of sparse foreground motion from dynamic background component. We further apply alternating direction method of multipliers algorithm to solve the proposed model. Extensive experiment results demonstrate the superiority of our method over some representative methods.