PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Evaluation of Probabilistic Occupancy Map People Detection for Surveillance Systems
Jérôme Berclaz, Ali Shahrokni, Francois Fleuret, James Ferryman and Pascal Fua
In: PETS 2009(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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:7330
Deposited By:Francois Fleuret
Deposited On:17 March 2011