Online feature weighting for human discrimination in a person following robot

Authors:
V. Alvarez-Santos;X. M. Pardo;R. Iglesias;A. Canedo-Rodriguez;C. V. Regueiro
Affiliations:
Department of Electronics and Computer Science, Universidade de Santiago de Compostela, Spain;Department of Electronics and Computer Science, Universidade de Santiago de Compostela, Spain;Department of Electronics and Computer Science, Universidade de Santiago de Compostela, Spain;Department of Electronics and Computer Science, Universidade de Santiago de Compostela, Spain;Department of Electronics and Systems, Universidade da Coruña, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Year:
2011

Citing 6
Cited 1

A computational approach to edge detection

Readings in computer vision: issues, problems, principles, and paradigms
Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features

IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural person-following behavior for social robots

Proceedings of the ACM/IEEE international conference on Human-robot interaction
Multisensor-based human detection and tracking for mobile service robots

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing

Feature analysis for human recognition and discrimination: Application to a person-following behaviour in a mobile robot

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A robust and adaptive person-following behaviour is an important ability that most service robots must have to be able to face challenging illumination conditions, and crowded spaces of nonstructured environments. In this paper, we propose a system which combines a laser based tracker with the support of a camera, acting as a discriminator between the target, and the other people present in the scene which might cause the laser tracker to fail. The discrimination is done using a online weighting of the feature space, based on the discriminability of each feature analysed.