Stochastic approach based salient moving object detection using kernel density estimation

Background modeling techniques are important for object detection and tracking in video surveillances. Traditional background subtraction approaches are suffered from problems, such as persistent dynamic backgrounds, quick illumination changes, occlusions, noise etc. In this paper, we address the problem of detection and localization of moving objects in a video stream without apperception of background statistics. Three major contributions are presented. First, introducing the sequential Monte Carlo sampling techniques greatly reduce the computation complexity while compromise the expected accuracy. Second, the robust salient motion is considered when resampling the feature points by removing those who do not move in a relative constant velocity and emphasis those in consistent motion. Finally, the proposed joint feature model enforced spatial consistency. Promising results demonstrate the potentials of the proposed algorithm.