Using boosted features for the detection of people in 2D range data

Arras, Kai O. and Martinez Mozos, Oscar and Burgard, Wolfram (2007) Using boosted features for the detection of people in 2D range data. In: 2007 IEEE International Conference on Robotics and Automation (ICRA'07), 10-14 April 2007, Rome, Italy.

Full content URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...

Documents
arras2007icra.pdf
[img] PDF
arras2007icra.pdf
Restricted to Repository staff only

588kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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 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.

Keywords:people detection, mobile robotics
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:9575
Deposited On:29 May 2013 14:29

Repository Staff Only: item control page