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Found 2 papers, 1 papers with code
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
Specifically, we propose a tempered Gibbs sampler that exploits the structure of the GLMB filtering density to achieve an $\mathcal{O}(T(P+M))$ complexity, where $T$ is the number of iterations of the algorithm, $P$ and $M$ are the number hypothesized objects and measurements.
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process.
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