A Trainable Pedestrian Detection System

In the near future, we can expect on-board automotive vision systems that inform or alert the driver about pedestrians, track surrounding vehicles, and read street signs. Object detection is fundamental to the success of this type of next-generation vision system. In this paper, we present a trainable object detection system that automatically learns to detect objects of a certain class in unconstrained scenes. We apply our system to the task of pedestrian detection. Unlike previous approaches to pedestrian detection that rely heavily on hand-crafted models and motion information, our system learns the pedestrian model from examples and uses no motion cues. The system can easily be extended to include motion information. We review our previous system, describe a new system that exhibits significantly better performance, provide a comparison between using different combinations of feature sets with classifiers of varying complexity, and describe improvements that increase the system’s processing speed by two orders of magnitude.