Evaluation of probabilistic occupancy map people detection for surveillance systems
Jérôme Berclaz, Ali Shahrokni, François Fleuret, James M. Ferryman, Pascal Fua · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2009
In 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. 1