Centralized fusion for fast people detection in dense environment

Authors:
Gwennael Gate;Amaury Breheret;Fawzi Nashashibi
Affiliations:
Robotics Laboratory, Mines ParisTech, Paris;Robotics Laboratory, Mines ParisTech, Paris;Robotics Laboratory, Mines ParisTech and INRIA, Le Chesnay
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.