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International Journal of Social Robotics

, Volume 2, Issue 1, pp 31–40 | Cite as

Multi-Part People Detection Using 2D Range Data

  • Oscar Martinez Mozos
  • Ryo Kurazume
  • Tsutomu Hasegawa
Article

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.

Laser-based people detection Multiple cue classification Sensor fusion Multi-part object detection 

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Copyright information

© Springer Science & Business Media BV 2009

Authors and Affiliations

  • Oscar Martinez Mozos
    • 1
  • Ryo Kurazume
    • 2
  • Tsutomu Hasegawa
    • 2
  1. 1.Dept. of Computer Science and System EngineeringUniversity of ZaragozaZaragozaSpain
  2. 2.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan

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