# Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

arXiv: https://arxiv.org/abs/2008.01667 · HTML: https://mlsanity.com/paper/2008.01667/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Extended target tracking utilizing machine-learning software -- with applications to animal classification](https://mlsanity.com/paper/2310.08316/) (2023)
- [Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing](https://mlsanity.com/paper/2310.12407/) (2023)

## Abstract (arXiv)

Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements of the target states provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.