A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The utilisation of a network of heterogeneous sensors to monitor human activity in a large space is essential due to the important field of view to be covered and the possible cluttered environment. The interpretation of this high number of data requires fast and powerful fusion algorithms in order to make easier the next human or computer work. In this paper the utilisation of a probabilistic occupancy map is proposed to fuse data coming from infrared and visible cameras. By estimating the occupancy and the velocity of each spatial cell representing the environment and thanks to a background subtraction algorithm, it is shown that human can be efficiently tracked. The architecture presented provides necessary information about pedestrians to perform, in the very near future, a human behaviour recognition step.