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arXiv · 1604.01202

Multi-object Tracking for Generic Observation Model Using Labeled Random Finite Sets

Suqi Li·Wei Yi·Reza Hoseinnezhad·Bailu Wang·Lingjiang Kong
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Abstract

This paper presents an exact Bayesian filtering solution for the multi-object tracking problem with the generic observation model. The proposed solution is designed in the labeled random finite set framework, using the product styled representation of labeled multi-object densities, with the standard multi-object transition kernel and no particular simplifying assumptions on the multi-object likelihood. Computationally tractable solutions are also devised by applying a principled approximation involving the replacement of the full multi-object density with a labeled multi-Bernoulli density that minimizes the Kullback-Leibler divergence and preserves the first-order moment. To achieve the fast performance, a dynamic grouping procedure based implementation is presented with a step-by-step algorithm. The performance of the proposed filter and its tractable implementations are verified and compared with the state-of-the-art in numerical experiments.

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Suqi Li, Wei Yi, Reza Hoseinnezhad, Bailu Wang, Lingjiang Kong. 2016-04-05. Multi-object Tracking for Generic Observation Model Using Labeled Random Finite Sets. https://arxiv.org/abs/1604.01202

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