Kai O. Arras, Óscar Martínez Mozos and Wolfram Burgard.
Using Boosted Features for the Detection of People in 2D Range Data.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA).
Rome, Italy, 2007.
pp. 3402-3407. ISBN: 1-4244-0602-1.
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.
Paper: [pdf: 531k]
Bibtex
@InProceedings{arras2007icra, title = {Using Boosted Features for the Detection of People in {2D} Range Data}, author = {Kai O. Arras and Oscar Martinez Mozos and Wolfram Burgard}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation}, year = {2007}, pages = {3402--3407}, url = {http://www.informatik.uni-freiburg.de/~omartine/publications/arras2007icra.pdf}, }
Multimedia
One-shot people detection using laser.
Beam colors:
Black: corrected classified as Non people.
Green: corrected classified as People.
Red: false classified.
Video: [avi: 918k].