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Using Boosted Features for the Detection of People in 2D Range Data Kai O. Arras Oscar Martinez Mozos Wolfram Burgard
 

Summary: Using Boosted Features for the Detection of People in 2D Range Data
Kai O. Arras ´Oscar Mart´inez Mozos Wolfram Burgard
University of Freiburg, Department of Computer Science, D-79110 Freiburg
{arras|omartine|burgard}@informatik.uni-freiburg.de
Abstract-- 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 su-
pervised 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.
I. INTRODUCTION
Detecting people is a key capacity for robots that operate
in populated environments. Knowledge about presence, po-
sition, and motion state of people will enable robots to better
understand and anticipate intentions and actions.

  

Source: Arras, Kai O. - Institut für Informatik, Albert-Ludwigs-Universität Freiburg

 

Collections: Computer Technologies and Information Sciences