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.

本文提出了一个以神经网络为基础的端到端框架，用于解决在线多目标跟踪中的数据关联问题。算法将帧间数据关联建模成最大带权二分匹配问题，并利用预先学习好的神经网络进行求解，该网络结合了外观和运动特征来计算有关联所需的邻接矩阵。实验表明，该框架能够提供更快的计算速度和更好的跟踪精度。