AUTOMATIC PEDESTRIAN SEGMENTATION COMBINING SHAPE, PUZZLE AND APPEARANCE

In this paper, we address the problem of automatically segmenting non-rigid pedestrians in still images. Since this task is well known difficult for any type of model or cue alone, a novel approach utilizing shape, puzzle and appearance cues is presented. The major contribution of this approach lies in the combination of multiple cues to refine pedestrian segmentation successively, which has two characterizations: (1) a shape guided puzzle integration scheme, which extracts pedestrians via assembling puzzles with constraint of a shape template; (2) a pedestrian refinement scheme, which is fulfilled by optimizing an automatically generated trimap that encodes both human silhouette and skeleton. Qualitative and quantitative evaluations on several public datasets verify the approach's effectiveness to various articulated bodies, human appearance and partial occlusion, and that this approach is able to segment pedestrians more accurately than methods based only on appearance or shape cue.