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Found 4 papers, 1 papers with code
The Smooth Trajectory Estimator for LMB Filters
In particular, we construct a smooth-trajectory estimator (i. e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time.
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
To address such problems, a divide-and-conquer manner has been employed with parallel computation.
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.
