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Markovian Tracking-by-Detection from a Single, Uncalibrated Camera

Michael D. Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, Luc Van Gool
Int. IEEE CVPR Workshop on Performance Evaluation of Tracking and Surveillance (PETS'09)
June 2009


We present an algorithm for multi-person tracking-bydetection in a particle filtering framework. To address the unreliability of current state-of-the-art object detectors, our algorithm tightly couples object detection, classification, and tracking components. Instead of relying only on the final, sparse output from a detector, we additionally employ its continuous intermediate output to impart our approach with more flexibility to handle difficult situations. The resulting algorithm robustly tracks a variable number of dynamically moving persons in complex scenes with occlusions. The approach does not rely on background modeling and is based only on 2D information from a single camera, not requiring any camera or ground plane calibration. We evaluate the algorithm on the PETS┬┐09 tracking dataset and discuss the importance of the different algorithm components to robustly handle difficult situations.

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  author = {Michael D. Breitenstein and Fabian Reichlin and Bastian Leibe and Esther Koller-Meier and Luc Van Gool},
  title = {Markovian Tracking-by-Detection from a Single, Uncalibrated Camera},
  booktitle = {Int. IEEE CVPR Workshop on Performance Evaluation of Tracking and Surveillance (PETS'09)},
  year = {2009},
  month = {June},
  keywords = {}