Real time vision based multi-person tracking for mobile robotics and intelligent vehicles

Authors:
Dennis Mitzel;Georgios Floros;Patrick Sudowe;Benito van der Zander;Bastian Leibe
Affiliations:
UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany
Venue:
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Year:
2011

Citing 10
Cited 0

Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Minimal Solution to the Generalised 3-Point Pose Problem

Journal of Mathematical Imaging and Vision
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

International Journal of Computer Vision
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

International Journal of Computer Vision
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Multiperson Tracking from a Mobile Platform

IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-3D-based on-board pedestrian detection system

Computer Vision and Image Understanding
Efficient large-scale stereo matching

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Efficient use of geometric constraints for sliding-window object detection in video

ICVS'11 Proceedings of the 8th international conference on Computer vision systems

Quantified Score

Hi-index	0.00

Visualization

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.