Multicamera People Tracking with a Probabilistic Occupancy Map

Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2008-02, Vol.30 (2), p.267-282
Main Authors: Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Cameras
Computer science
control theory
systems
Dynamic programming
Exact sciences and technology
Frames
Head
Hidden Markov Model
Hidden Markov models
Humans
Illumination
Intelligence
Loss measurement
Multi-camera
Multi-people tracking
Occlusion
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Probabilistic occupancy map
Robustness
Streaming media
Streams
Tracking
Trajectories
Trajectory
Video surveillance
Visual surveillance
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Summary:Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2007.1174