Issue No. 09 - September (2011 vol. 33)
Michael D. Breitenstein , ETH Zurich, Zurich
Fabian Reichlin , LiberoVision AG, Zurich
Bastian Leibe , RWTH Aachen University, Aachen
Esther Koller-Meier , ETH Zurich, Zurich
Luc Van Gool , ETH Zurich, Zurich and ESAT-PSI/IBBT, KU Leuven
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.
Multi-object tracking, tracking-by-detection, detector confidence particle filter, pedestrian detection, particle filtering, sequential Monte Carlo estimation, online learning, detector confidence, surveillance, sports analysis, traffic safety.
M. D. Breitenstein, F. Reichlin, E. Koller-Meier, B. Leibe and L. V. Gool, "Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1820-1833, 2010.