In multi-object tracking, multiple objects generate multiple sensor
measurements, which are used to estimate the objects’ state
simultaneously. Since it is unknown from which object a measurement
originates, a data association problem arises. Considering all possible
associations is computationally infeasible for large numbers of objects
and measurements. Hence, approximation methods are applied to compute
the most relevant associations. Here, we focus on deterministic methods,
since multi-object tracking is often applied in safety-critical areas.
In this work we show that Herded Gibbs sampling, a deterministic version
of Gibbs sampling, applied in the labeled multi-Bernoulli filter, yields
results of the same quality as randomized Gibbs sampling while having
comparable computational complexity.
We conclude that it is a suitable deterministic alternative to
randomized Gibbs sampling and could be a promising approach for other
data association problems.