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Author:
Kleiner, Alexander (University of Freiburg)
Kümmerle, R. (University of Freiburg)
Title:
Genetic MRF Model Optimization for Real-Time Victim Detection in Search and Rescue
Publication type:
Conference paper (Refereed)
Language:
English
In:
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
Publisher: IEEE conference proceedings
Pages:
3025-3030
Year of publ.:
2007
URI:
urn:nbn:se:liu:diva-72560
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72560
ISBN:
978-1-4244-0912-9
Subject category:
Robotics
Project:
Artificial Intelligence & Integrated Computer Systems
Abstract(en) :

One primary goal in rescue robotics is to deploy a team of robots for coordinated victim search after a disaster. This requires robots to perform subtasks, such as victim detection, in real-time. Human detection by computationally cheap techniques, such as color thresholding, turn out to produce a large number of false-positives. Markov Random Fields (MRFs) can be utilized to combine the local evidence of multiple weak classifiers in order to improve the detection rate. However, inference in MRFs is computational expensive. In this paper we present a novel approach for the genetic optimizing of the building process of MRF models. The genetic algorithm determines offline relevant neighborhood relations with respect to the data, which are then utilized for generating efficient MRF models from video streams during runtime. Experimental results clearly show that compared to a Support Vector Machine (SVM) based classifier, the optimized MRF models significantly reduce the false-positive rate. Furthermore, the optimized models turned out to be up to five times faster then the non-optimized ones at nearly the same detection rate.

Available from:
2011-11-29
Created:
2011-11-28
Last updated:
2011-12-06
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