Markov Chain Monte Carlo Data Association for Multiple-Target Tracking

This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multiple-target 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 multiple-target 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. It exhibits remarkable performance compared to multiple hypothesis tracking (MHT) under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates

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