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Multi-Part People Detection Using 2D Range Data

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Abstract

People detection is a key capacity for robotics systems that have to interact with humans. This paper addresses the problem of detecting people using multiple layers of 2D laser range scans. Each layer contains a classifier able to detect a particular body part such as a head, an upper body or a leg. These classifiers are learned using a supervised approach based on AdaBoost. The final person detector is composed of a probabilistic combination of the outputs from the different classifiers. Experimental results with real data demonstrate the effectiveness of our approach to detect persons in indoor environments and its ability to deal with occlusions.

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Authors and Affiliations

  1. Dept. of Computer Science and System Engineering, University of Zaragoza, Zaragoza, Spain

    Oscar Martinez Mozos

  2. Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Ryo Kurazume & Tsutomu Hasegawa

Authors
  1. Oscar Martinez Mozos
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  2. Ryo Kurazume
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  3. Tsutomu Hasegawa
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Corresponding author

Correspondence to Oscar Martinez Mozos.

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Mozos, O.M., Kurazume, R. & Hasegawa, T. Multi-Part People Detection Using 2D Range Data. Int J of Soc Robotics 2, 31–40 (2010). https://doi.org/10.1007/s12369-009-0041-3

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  • DOI: https://doi.org/10.1007/s12369-009-0041-3

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