Unsupervised segmentation of stereoscopic video objects: investigation of two depth-based approaches

Abstract

Two unsupervised video object segmentation techniques are proposed and are compared in terms of computational cost and segmentation quality. Both methods are based on the exploitation of depth information. In particular a depth segments map is initially estimated by analyzing a stereoscopic pair of frames and applying a segmentation algorithm. Next, considering the first "constrained fusion of color segments" (CFCS) approach, color segmentation is performed to one of the stereo pairs and video objects are extracted by fusing color segments according to depth similarity. In the second method an active contour is automatically initialized onto the boundary of each depth segment, according to a fitness function that considers different color areas and preserves the shapes of depth segments' boundaries. Then the active contour moves onto a grid to extract the video object. Experiments on real stereoscopic sequences exhibit the speed and accuracy of the proposed schemes.