Detection and segmentation of moving objects in highly dynamic scenes
Detecting and segmenting moving objects in dynamic scenes is a hard but essential task in a number of applications such as surveillance. Most existing methods only give good results in the case of persistent or slowly changing background, or if both the objects and the background are rigid. In this paper, we propose a new method for direct detection and segmentation of foreground moving objects in the absence of such constraints. First, groups of pixels having similar motion and photometric features are extracted. For this first step only a sub-grid of image pixels is used to reduce computational cost and improve robustness to noise. We introduce the use of p-value to validate optical flow estimates and of automatic bandwidth selection in the mean shift clustering algorithm. In a second stage, segmentation of the object associated to a given cluster is performed in a MAP/MRF framework. Our method is able to handle moving camera and several different motions in the background. Experiments on challenging sequences show the performance of the proposed method and its utility for video analysis in complex scenes.