A Graph Neural Network Approach for Solving the Ranked Assignment Problem in Multi-Object Tracking
This addresses a computational bottleneck in data association for autonomous vehicles, but it is an incremental improvement over existing methods.
The paper tackles the ranked assignment problem in multi-object tracking for autonomous vehicles by proposing a Graph Neural Network (GNN) approach called RAPNet, which improves accuracy compared to the Gibbs sampler.
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 $δ$-Generalized Labeled Multi-Bernoulli ($δ$-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.