Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

By B. Vo, Dinh Q. Phung. IEEE Transactions on Signal Processing, 2013

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An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/76824c02f056b40f9e1b4b4787e4b134fa86e34b

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Citations in the corpus

Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “The second MTT conjugate prior is based on regular RFS s, i.e., unlabeled, and is called Poisson Multi-Bernoulli Mixture ( PMBM ) [205].”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “The efficiency of the approach is improved in [36], using a k-th shortest path algorithm to avoid enumeration of all global hypotheses upon prediction.”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “The first implementation of the GLMB filter truncates these sums by using the K-shortest path and ranked assignment algorithms, respectively, in the prediction and update to determine the most significant components [28].”
The Labeled Multi-Bernoulli Filter (2014, background): “A multi-target system is fundamentally different from a single-target system in that the number of states changes with time due to births and deaths of targets [1]–[3].”