# Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking

arXiv: https://arxiv.org/abs/2506.09441 · HTML: https://mlsanity.com/paper/2506.09441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Generalized optimal sub-pattern assignment metric](https://mlsanity.com/paper/1601.05585/) (2016)
- [A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms](https://mlsanity.com/paper/1605.01177/) (2016)
- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [Can Deep Learning be Applied to Model-Based Multi-Object Tracking?](https://mlsanity.com/paper/2202.07909/) (2022)
- [Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing](https://mlsanity.com/paper/2312.17261/) (2023)

## Abstract (arXiv)

Tracking multiple particles in noisy and cluttered scenes remains challenging due to a combinatorial explosion of trajectory hypotheses, which scales super-exponentially with the number of particles and frames. The transformer architecture has shown a significant improvement in robustness against this high combinatorial load. However, its performance still falls short of the conventional Bayesian filtering approaches in scenarios presenting a reduced set of trajectory hypothesis. This suggests that while transformers excel at narrowing down possible associations, they may not be able to reach the optimality of the Bayesian approach in locally sparse scenario. Hence, we introduce a hybrid tracking framework that combines the ability of self-attention to learn the underlying representation of particle behavior with the reliability and interpretability of Bayesian filtering. We perform trajectory-to-detection association by solving a label prediction problem, using a transformer encoder to infer soft associations between detections across frames. This prunes the hypothesis set, enabling efficient multiple-particle tracking in Bayesian filtering framework. Our approach demonstrates improved tracking accuracy and robustness against spurious detections, offering a solution for high clutter multiple particle tracking scenarios.