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Multicamera people tracking with a probabilistic occupancy map.

François Fleuret1, Jérôme Berclaz, Richard Lengagne

  • 1Ecole Polytechnique Fédérale de Lausanne, Station 14, CH-1015 Lausanne, Switzerland. francois.fleuret@epfl.ch

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 18, 2007
PubMed
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This study introduces a generative model combined with dynamic programming for accurate multi-person tracking, even with occlusions and lighting changes. The method reliably follows individuals and generates precise trajectories across video sequences.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-person tracking is challenging due to occlusions and varying lighting.
  • Existing methods struggle with accurately following multiple individuals over long sequences.

Purpose of the Study:

  • To develop an accurate multi-person tracking system using synchronized video streams.
  • To generate metrically accurate trajectories for tracked individuals.
  • To effectively handle occlusions and lighting variations.

Main Methods:

  • Combining a generative model with dynamic programming for multi-person tracking.
  • Processing individual trajectories separately over long sequences with a ranking heuristic.
  • Utilizing synchronized video streams from multiple viewpoints.

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Main Results:

  • Accurate tracking of up to six individuals across thousands of frames.
  • Effective handling of significant occlusions and lighting changes.
  • Derivation of metrically accurate trajectories for each individual.
  • Robust performance even with unknown numbers of individuals and simple background subtraction data.

Conclusions:

  • The proposed generative model and dynamic programming approach significantly improves multi-person tracking accuracy.
  • The method demonstrates resilience to occlusions and environmental variations.
  • Reliable multi-person tracking can be achieved by processing individual trajectories with a suitable heuristic.