Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
This work addresses the data association challenge for online multiple-object tracking, which is incremental as it builds on existing methods with a novel neural network integration.
The authors tackled the data association problem in online multiple-object tracking by proposing an end-to-end framework that formulates it as a maximum weighted bipartite matching problem, learned via a neural network with appearance and motion cues, and achieved improved adaptiveness and reduced parameter-tuning, though no specific performance numbers are provided in the abstract.
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame data association as Maximum Weighted Bipartite Matching problem, whose solution is learned using a neural network. The network incorporates an affinity learning module, wherein both appearance and motion cues are investigated to encode object feature representation and compute pairwise affinities. Employing the computed affinities as edge weights, the following matching problem on a bipartite graph is resolved by the optimization module, which leverages a graph neural network to adapt with the varying cardinalities of the association problem and solve the combinatorial hardness with favorable scalability and compatibility. To facilitate effective training of the proposed tracking network, we design a multi-level matrix loss in conjunction with the assembled supervision methodology. Being trained end-to-end, all modules in the tracker can co-adapt and co-operate collaboratively, resulting in improved model adaptiveness and less parameter-tuning efforts. Experiment results on the MOT benchmarks demonstrate the efficacy of the proposed approach.