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：我们提出了一种基于图的多目标跟踪（MOT）框架，其中目标检测包含运动学信息和重识别特征（属性）。我们的方法将连续最短路径（SSP）算法应用于定义在帧批次上的跟踪图。该跟踪图中的边权值通过一个消息传递网络（图神经网络（GNN）的一种变体）计算得出。GNN（亦即跟踪器）的参数通过在由真实轨迹和检测示例组成的训练集上进行端到端学习得到。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了该算法，以探究其对场景要素和模型超参数的敏感性。在多种复杂度的场景下，我们的方法相较于强基线模型均表现出优越性能。