Pedestrian Detection in Binocular Stereo Sequence Based on Appearance Consistency

Pedestrian detection is an important yet challenging task. In this paper, a pedestrian codetection framework for binocular stereo sequences is proposed. Binocular vision and consecutive frames can provide more information than a single image. That information can be used to improve detection performance by reducing the number of candidate detection windows with low confidence or enhancing the high-confidence candidates. To design the framework, we follow the intuition that a pedestrian has consistent appearance when observed from the same or different viewpoints. First, a baseline detector is used in both stereo images with a conservative threshold in order to expand the set of detection candidates. Before the detection process, an assisted stixel world model is computed for both left and right frames. Thus, the search range of the detector is greatly reduced with the help of the stixel world. Second, adjacency-constrained patch matching is proposed to build correspondence between two candidates in both intra and inter sequences of binocular vision. Finally, we establish a mechanism to update the score of the detection aided by the corresponding candidates. The experimental results show that our framework significantly improves the performance of the evaluated baseline pedestrian approaches.