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.

Because pedestrians have neither well defined shapes nor well defined behaviors, detecting and tracking them from a moving vehicle remains a difficult task. To serve as an onboard driver assistance system, a perception algorithm also needs to be both fast and robust. We present in this paper a system that reaches a good level of reliability by efficiently combining the data of two sensors -a laser scanner and a camera -while remaining tractable on CPU limited mobile architectures.

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