Detection and Tracking of Multiple Pedestrians in Automotive Applications

Abstract

We present a method for tracking an unknown and changing number of far away pedestrians in a video stream. Multiple particle filter instances are utilized which track single pedestrians independently from each other. The tracking is guided by a cascade classifier which is integrated into the particle filter framework. In order to be able to detect hardly visible pedestrians and to filter out isolated false positives of the classifier, we developed a detection criterion for particle filters which follows the track-before-detect paradigm. The system nearly works in real time.