%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Berclaz_PETS_2009,
                      author = {Berclaz, Jerome and Shahrokni, Ali and Fleuret, Francois and Ferryman, James and Fua, Pascal},
                    projects = {Idiap},
                       title = {Evaluation of Probabilistic Occupancy Map People Detection for Surveillance Systems},
                   booktitle = {Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance},
                        year = {2009},
                    abstract = {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.}
}