Motion-Aware Graph Regularized RPCA for background modeling of complex scenes

Computing a background model from a given sequence of video frames is a prerequisite for many computer vision applications. Recently, this problem has been posed as learning a low-dimensional subspace from high dimensional data. Many contemporary subspace segmentation methods have been proposed to overcome the limitations of the methods developed for simple background scenes. Unfortunately, because of the absence of motion information and without preserving intrinsic geometric structure of video data, most existing algorithms do not provide promising nature of the low-rank component for complex scenes. Such as largely occluded background by foreground objects, superfluity in video frames in order to cope with intermittent motion of foreground objects, sudden lighting condition variation, and camera jitter sequences. To overcome these difficulties, we propose a motion-aware regularization of graphs on low-rank component for video background modeling. We compute optical flow and use this information to make a motion-aware matrix. In order to learn the locality and similarity information within a video we compute inter-frame and intra-frame graphs which we use to preserve geometric information in the low-rank component. Finally, we use linearized alternating direction method with parallel splitting and adaptive penalty to incorporate the preceding steps to recover the model of the background. Experimental evaluations on challenging sequences demonstrate promising results over state-of-the-art methods.