Using Boosted Features for the Detection of People in 2D Range Data

This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the de...

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Bibliographic Details
Published inProceedings 2007 IEEE International Conference on Robotics and Automation pp. 3402 - 3407
Main Authors Arras, K.O., Mozos, O.M., Burgard, W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Computer science
Computer vision
Data mining
Humans
Laser beams
Laser modes
Leg
Robot sensing systems
Robotics and automation
Supervised learning
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ISBN1424406013
9781424406012
ISSN1050-4729
DOI10.1109/ROBOT.2007.363998

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Summary:This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the detection of people. In particular, our approach applies AdaBoost to train a strong classifier from simple features of groups of neighboring beams corresponding to legs in range data. Experimental results carried out with laser range data illustrate the robustness of our approach even in cluttered office environments
ISBN:1424406013
9781424406012
ISSN:1050-4729
DOI:10.1109/ROBOT.2007.363998