Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment
Fuliang Li, Ronghui Zhang, Feng You · IET Image Processing · 2017
Pedestrian detection has become one of the hottest topics in intelligent traffic system because of its potential applications in driver assistance and automatic driving. In this study, a fast pedestrian detection and dynamic tracking method within vehicle‐to‐vehicle (V2V) cooperative environment is proposed. A dynamic tracking‐by‐detection framework for real‐time pedestrian detection is developed. First, a cascade classifiers, based on selected Haar‐like features, is trained to detect pedestrian. Then, CamShift algorithm combined with extended Kalman filtering is used to pedestrian dynamic tracking. Finally, with the crowdsourcing detected information, a smartphone‐based V2V cooperative warning system is developed to share useful detection results within blind spots. The experiment results show that the proposed method has a real‐time and accurate performance, which can provide a reference for road traffic safety monitoring technology.