Standard MOT solves: [ \max_\mathbfX \sum_i,j S_ij x_ij \quad \texts.t. flow conservation constraints, ] which is a min-cost flow / assignment problem. This becomes intractable for dense scenes.
We presented , a polynomial-time MOT framework with graph-positive Laplacian regularization. PTG+ balances efficiency and accuracy, especially in dense scenes. Future work includes extending to online learning of (\epsilon) and integration with transformer-based detectors. poly track gplus
The game adopts a "low-poly" art style. Think early PlayStation 1 graphics but smoothed out and stylized. The tracks are colorful, the cars are blocky, and the frame rate is buttery smooth. It feels retro without being dated. Standard MOT solves: [ \max_\mathbfX \sum_i,j S_ij x_ij