Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking

TL;DR

This paper presents a hybrid particle tracking method combining transformer-based association learning with Bayesian filtering to improve accuracy and robustness in cluttered, noisy scenes.

Contribution

It introduces a novel hybrid framework that leverages transformers for association and Bayesian filtering for hypothesis pruning, enhancing multiple particle tracking performance.

Findings

Improved tracking accuracy in cluttered scenes

Enhanced robustness against false detections

Efficient hypothesis pruning with hybrid approach

Abstract

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.