Evaluation of Probabilistic Occupancy Map People Detection for Surveillance Systems
| Type of publication: | Conference paper |
| Citation: | Berclaz_PETS_2009 |
| 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. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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