  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.
