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Deterministic Gibbs Sampling for Data Association in Multi-Object Tracking

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posted on 09.09.2020, 13:27 by Laura M. Wolf, Marcus Baum
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.

Funding

This work was funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3493 within the Lower Saxony “Vorab“ of the Volkswagen Foundation and supported by the Center for Digital Innovations (ZDIN).

History

Email Address of Submitting Author

laura.wolf@cs.uni-goettingen.de

Submitting Author's Institution

University of Göttingen

Submitting Author's Country

Germany

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