Background estimation and motion saliency detection using total variation-based video decomposition

As human vision system is highly sensitive to motion present in a scene, motion saliency forms an important feature in a video sequence. Motion information is used for video compression, object segmentation, object tracking and in many other applications. Though its applications are extensive, accurate detection of motion in a given video is complex and computationally expensive for the solutions reported in the literature. Decomposing a video into visually similar and residual videos is a robust way to detect motion salient regions. The existing decomposition techniques require large execution time as the standard form of the problem is NP-hard. We propose a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. In addition, the proposed algorithm is completely parallelizable that ensures further reduction in computational time with the use of advanced multicore processors.