Shape and motion-based pedestrian detection in infrared images: a multi sensor approach

Abstract

This work deals with the detection and tracking of pedestrians. The focus of the investigations was on methods, which allow a precise and detailed description of both significant features of pedestrians: shape and motion. Since the practical employment of such methods requires a good initialization and tracking, a multi sensor system was developed consisting of a far infrared camera, a laser scanning device and ego motion sensors. To handle the combination of the information of the different sensors a Kalman filter based data fusion is used. Arranging a set of Kalman filters in parallel, a multi sensor/multi target tracking system was created. The system structure combines a straightforward with a backward loop methodology to combine fast initiation functions with more affordable verification functions. Therefore formerly known semiautomatic image processing methods work fully automatically in the system. The analysis of the estimated optical flow regarding the typical human motion as well as the analysis of shape parameters using active contour models is performed. The multi sensor/multi target tracking system is installed on a test vehicle to obtain practical results, which are also discussed in this article.