Markov Chain Monte Carlo Data Association for Multi-Target Tracking
Songhwai Oh
Russell, S.
Sastry, S.
Electr. Eng. & Comput. Sci., Univ. of California, Merced, CA;
This paper appears in: Automatic Control, IEEE Transactions on
Publication Date: March 2009
Volume: 54,
Issue: 3
On page(s): 481-497
ISSN: 0018-9286
INSPEC Accession Number: 10505312
Digital Object Identifier: 10.1109/TAC.2009.2012975
First Published: 2009-03-04
Current Version Published: 2009-03-10
Abstract
This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For general multitarget tracking problems, in which unknown numbers of targets appear and disappear at random times, we present a multi-scan MCMCDA algorithm that approximates the optimal Bayesian filter. We also present extensive simulation studies supporting theoretical results in this paper. Our simulation results also show that MCMCDA outperforms multiple hypothesis tracking (MHT) by a significant margin in terms of accuracy and efficiency under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
