Occlusion-Based Accurate Silhouettes from Video Streams

Abstract

AbstractWe address the problem of segmenting out moving objects from video. The majority of current approaches use only the image motion between two consecutive frames and fail to capture regions with low spatial gradient, i.e., low textured regions. To overcome this limitation, we model explicitly: i) the occlusion of the background by the moving object and ii) the rigidity of the moving object across a set of frames. The segmentation of the moving object is accomplished by computing the Maximum Likelihood (ML) estimate of its silhouette from the set of video frames. To minimize the ML cost function, we developed a greedy algorithm that updates the object silhouette, converging in few iterations. Our experiments with synthetic and real videos illustrate the accuracy of our segmentation algorithm.KeywordsVideo SequenceInitial GuessMotion EstimationVideo StreamActive ContourThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.