  This letter focuses on the task of Multi-Target Multi-Camera vehicle
tracking. We propose to associate single-camera trajectories into multi-camera
global trajectories by training a Graph Convolutional Network. Our approach
simultaneously processes all cameras providing a global solution, and it is
also robust to large cameras unsynchronizations. Furthermore, we design a new
loss function to deal with class imbalance. Our proposal outperforms the
related work showing better generalization and without requiring ad-hoc manual
annotations or thresholds, unlike compared approaches.
