Abstract
Associating measurements with tracks is a crucial step in Multi-Object Tracking (MOT) to guarantee the safety of autonomous vehicles. To manage the exponentially growing number of track hypotheses, truncation becomes necessary. In the $\delta$-Generalized Labeled Multi-Bernoulli ($\delta$-GLMB) filter application, this truncation typically involves the ranked assignment problem, solved by Murty's algorithm or the Gibbs sampling approach, both with limitations in terms of complexity or accuracy, respectively. With the motivation to improve these limitations, this paper addresses the ranked assignment problem arising from data association tasks with an approach that employs Graph Neural Networks (GNNs). The proposed Ranked Assignment Prediction Graph Neural Network (RAPNet) uses bipartite graphs to model the problem, harnessing the computational capabilities of deep learning. The conclusive evaluation compares the RAPNet with Murty's algorithm and the Gibbs sampler, showing accuracy improvements compared to the Gibbs sampler.
Abstract (translated)
将量测与航迹关联是多目标跟踪（MOT）中确保自动驾驶车辆安全的关键步骤。为应对呈指数级增长的航迹假设数量，截断成为必要操作。在δ-广义标签多伯努利（δ-GLMB）滤波器的应用中，该截断通常涉及排序分配问题，可通过Murty算法或吉布斯采样方法求解，但二者分别在复杂度或精度方面存在局限。为改善这些局限，本文采用图神经网络（GNNs）的方法处理数据关联任务中产生的排序分配问题。所提出的排序分配预测图神经网络（RAPNet）利用二分图对问题进行建模，并发挥深度学习的计算能力。最终评估将RAPNet与Murty算法及吉布斯采样器进行比较，结果显示相较于吉布斯采样器在精度上有所提升。
URL
https://arxiv.org/abs/2604.01696
