A. Kleiner and R. K├╝mmerle.
Genetic MRF model optimization for real-time victim detection in Search and Rescue.
In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS). San Diego, CA, USA, November 2007, pages 3025-3030.

Abstract

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 classiffiers in order to improve the detection rate. However, inference in MRFs is com- putational expensive. In this paper we present a novel approach for the genetic optimizing of the building process of MRF models. The genetic algorithm determines online 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 classiffier, the optimized MRF models signifficantly 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.

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BibTeX entry:

@inproceedings{kleiner07iros,
  author = {Kleiner, A. and K{\"u}mmerle, R.},
  title = {Genetic {MRF} model optimization for real-time victim detection in Search and
     Rescue},
  booktitle = {Proc. of the {IEEE/RSJ} Int. Conf. on Intelligent Robots and Systems {(IROS)}},
  year = {2007},
  month = {November},
  address = {San Diego, CA, USA},
  pages = {3025--3030}
}