Evaluation of a posteriori probabilities of multi-frame data association hypotheses

This paper discusses the problem of numerically evaluating multi-frame, data-association hypotheses in multiple-target tracking in terms of their a posteriori probabilities. We describe two approaches to the problem: (1) an approach based on K-best multi-frame data association hypothesis selection algorithms, and (2) a more direct approach to calculating a posteriori probabilities through Markov-chain-Monte-Carlo (MCMC) or sequential Monte Carlo (SMC) methods. This paper defines algorithms based on those two approaches and compares their performance, and it discusses their relative effectiveness, using simple numerical examples.

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