Can appearance patterns improve pedestrian detection?

This paper studies the usefulness of appearance patterns for the challenging task of pedestrian detection. Despite appearance specific models being common in rigid object detection, the technique is still little understood for pedestrians. Three main approaches for reasoning over orientation, occlusion, and visual cues in obtaining the appearance patterns are compared. This work demonstrates that large gains in detection performance (up to 17 AP points on the challenging KITTI dataset) can be made using a state-of-the-art pedestrian detector.