Layered Object Recognition System for Pedestrian Sensing
Abstract
There is a significant need to develop innovative technologies to detect pedestrians or other vulnerable road users at designated crossing locations and midblock/unexpected areas and to determine potential collisions with pedestrians. An in-vehicle pedestrian sensing system was developed to address this specific problem. The research team used stereo vision cameras and developed three key innovations, namely, the detection and recognition of multiple roadway objects; the use of multiple cues (depth, motion, shape, and appearance) to detect, track, and classify pedestrians; and the use of contextual information to reject a majority of the typical false positives that plague vision-based pedestrian detection systems. This report describes the approach and tabulates representative results of experiments conducted on multiple video sequences captured over the course of the project. The conclusion derived from these results is that the developed system is state of the art when compared to the best approaches published in literature. The false positive rates are still higher than desired for the system to be ready for commercialization. This report also provides steps that can be taken to improve the performance in this regard. A real-time system was developed and demonstrated in a test vehicle.