Multi-part people detection using 2D range data

Oscar Martinez Mozos, Ryo Kurazume, Tsutomu Hasegawa

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


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 Ada Boost. 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.

Original languageEnglish
Pages (from-to)31-40
Number of pages10
JournalInternational Journal of Social Robotics
Issue number1
Publication statusPublished - Mar 2010

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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