Pedestrian Detection at Warp Speed: Exceeding 500 Detections per Second
Object detection, and in particular pedestrian detection, is a challenging task, due to the wide variety of appearances. The application domain is extremely broad, ranging from e.g. surveillance to automotive safety systems. Many practical applications however often rely on stringent real-time processing speeds combined with high accuracy needs. These demands are contradictory, and usually a compromise needs to be made. In this paper we present a pedestrian detection framework which is extremely fast (500 detections per second) while still maintaining excellent accuracy results. We achieve these results by combining our fast pedestrian detection algorithm (implemented as a hybrid CPU and GPU combination) with the exploitation of scene constraints (using a warping window approach and temporal information), which yields state-of-the-art detection accuracy. We present profound evaluation results of our algorithm concerning both speed and accuracy on the challenging Caltech dataset. Furthermore we present evaluation results on a very specific application showing the full potential of this warping window approach: detection of pedestrians in a truck's blind spot zone.