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

arXiv: https://arxiv.org/abs/1604.01202 · HTML: https://mlsanity.com/paper/1604.01202/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 1 of 1)

- [Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering](https://mlsanity.com/paper/2211.16041/) (2022)

## Abstract (arXiv)

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.