Evaluation of probabilistic occupancy map people detection for surveillance systemsBerclaz, J., Shahrokni, A., Fleuret, F., Ferryman, J. and Fua, P. (2009) Evaluation of probabilistic occupancy map people detection for surveillance systems. In: Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Miami, Florida, USA, pp. 55-62. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryIn this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.
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