A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle

In this paper we describe a fully integrated system for detecting, localizing, and tracking pedestrians from a moving vehicle. The system can reliably detect upright pedestrians to a range of 40 m in lightly cluttered urban environments. The system uses range data from stereo vision to segment the scene into regions of interest, from which shape features are extracted and used to classify pedestrians. The regions are tracked using shape and appearance features. Tracking is used to temporally filter classifications to improve performance and to estimate the velocity of pedestrians for use in path planning. The end-to-end system runs at 5 Hz on 1 ,024 × 768 imagery using a standard 2.4 GHz Intel Core 2 Quad processor, and has been integrated and tested on multiple ground vehicles and environments. We show performance on a diverse set of datasets with groundtruth in outdoor environments with varying degrees of pedestrian density and clutter. In highly cluttered urban environments, the detection rates are on a par with state-of-the-art but significantly slower systems.