Fusing multiple image transformations and a thermal sensor with kinect to improve person detection ability

Authors:
L. Susperregi;A. Arruti;E. Jauregi;B. Sierra;J. M. MartíNez-Otzeta;E. Lazkano;A. Ansuategui
Affiliations:
-;-;-;-;-;-;-
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 13
Cited 0

Instance-Based Learning Algorithms

Machine Learning
Original Contribution: Stacked generalization

Neural Networks
Bagging predictors

Machine Learning
Feature subset selection by Bayesian network-based optimization

Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
An Experimental Study on Pedestrian Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach

Decision Support Systems
Laser Based People Following Behaviour in an Emergency Environment

ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Face recognition in multi-sensor images based on a novel modular feature selection technique

Information Fusion
On exploration of classifier ensemble synergism in pedestrian detection

IEEE Transactions on Intelligent Transportation Systems
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel approach to combine data from multiple low-cost sensors to detect people in a mobile robot. Robust detection of people is a key capability required for robots working in environments with people. Several works have shown the benefits of fusing data from complementary sensors. The Kinect sensor provides a rich data set at a significantly low cost, however, it has some limitations for its use on a mobile platform, mainly that people detection algorithms rely on images captured by a static camera. To cope with these limitations, this work is based on the fusion of Kinect and a thermical sensor (thermopile) mounted on top of a mobile platform. We propose the implementation of an evolutionary selection of sequences of image transformation to detect people through supervised classifiers. Experimental results carried out with a mobile platform in a manufacturing shop floor show that the percentage of wrong classified using only Kinect is drastically reduced with the classification algorithms and with the combination of the three information sources. Extra experiments are presented as well to show the benefits of the image transformation sequence idea here presented.