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Robust Tracking-by-Detection using a Detector Confidence Particle Filter

Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
IEEE International Conference on Computer Vision
October 2009

Abstract

We propose a novel approach for multi-person tracking-by-detection in a particle filtering framework. Our algorithm builds upon the intermediate confidence density of state-of-the-art pedestrian detectors and robustly integrates it into the observation model. This generic object category knowledge is complemented by instance-specific classifiers that are used to resolve data association issues and to adapt particle weights. The resulting algorithm robustly tracks a large number of dynamically moving persons in complex scenes with partial and complete occlusions, does not rely on background modeling, and operates entirely in 2D (requiring no camera or ground plane calibration). We quantitatively evaluate our approach¿s performance on a variety of challenging datasets and show that it improves upon state-of-the-art methods from the literature.


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@InProceedings{eth_biwi_00633,
  author = {Michael D. Breitenstein and Fabian Reichlin and Bastian Leibe and Esther Koller-Meier and Luc Van Gool},
  title = {Robust Tracking-by-Detection using a Detector Confidence Particle Filter},
  booktitle = {IEEE International Conference on Computer Vision},
  year = {2009},
  month = {October},
  keywords = {}
}