Real Time Vision Based Multi-person Tracking for Mobile Robotics and Intelligent Vehicles

Dennis Mitzel, Georgios Floros, Patrick Sudowe, Benito van der Zander, Bastian Leibe

Abstract: In this paper, we present a real-time vision-based multi-person tracking system working in crowded urban environments. Our approach combines stereo visual odometry estimation, HOG pedestrian detection, and multi-hypothesis tracking-by-detection to a robust tracking framework that runs on a single laptop with a CUDA-enabled graphics card. Through shifting the expensive computations to the GPU and making extensive use of scene geometry constraints we could build up a mobile system that runs with 10Hz. We experimentally demonstrate on several challenging sequences that our approach achieves competitive tracking performance.