We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-identification features (attributes). Our method applies a successive shortest paths (SSP) algorithm to a tracking graph defined over a batch of frames. The edge costs in this tracking graph are computed via a message-passing network, a graph neural network (GNN) variant. The parameters of the GNN, and hence, the tracker, are learned end-to-end on a training set of example ground-truth tracks and detections. Specifically, learning takes the form of bilevel optimization guided by our novel loss function. We evaluate our algorithm on simulated scenarios to understand its sensitivity to scenario aspects and model hyperparameters. Across varied scenario complexities, our method compares favorably to a strong baseline.

翻译：本文提出一种基于图结构的多目标跟踪（MOT）建模方法，其中目标检测包含运动学信息与重识别特征（属性）。本方法将连续最短路径（SSP）算法应用于跨多帧构建的跟踪图，该图中各边的权重通过消息传递网络（图神经网络GNN的一种变体）计算得出。GNN的参数（亦即跟踪器参数）通过在包含真实轨迹与检测结果的训练集上进行端到端学习获得。具体而言，学习过程采用由我们提出的新型损失函数引导的双层优化形式。我们在仿真场景中评估算法性能，以探究其对场景特性及模型超参数的敏感性。在不同复杂度的场景中，本方法相较于强基线模型均表现出优越性能。