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Conference paper
Tracking Multiple Moving Objects Using Adaptive Sample-based Joint Probabilistic Data Association Filter
Proceedings of 5th International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2008), pp.93-98, 2008
ABSTRACT:
In this paper we present a probabilistic method for tracking multiple moving objects. Joint probabilistic data association filter is used for assignments between detected features and objects being tracked. A particle filter is used for representation of underlying object state uncertainty. Novelty of our approach is particle number adaptation. Experiments done on real world and simulated laser range data show that our algorithm is robust and accurate in tracking multiple objects.
BibTeX entry:
@inproceedings \{Juric-Kavelj2008_407,
author = \{Juri\'{c}-Kavelj, S. AND Seder, M. AND Petrovi\'{c}, I.},
title = \{Tracking Multiple Moving Objects Using Adaptive Sample-based Joint Probabilistic Data Association Filter},
booktitle = {Proceedings of 5th International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2008)},
pages = \{93-98},
year = \{2008}
}

 

 

 

 

 

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