Poly Track Gplus ~upd~ Instant

A. Chen, B. Kumar, C. Zhao Affiliation: Institute for Autonomous Systems & Data Fusion

PTG+ achieves 4.2% higher HOTA than GNN-MOT, with 31% fewer ID switches, and runs 3× faster. Removing the Gplus term → IDS increases by 48% and HOTA drops by 6.1%, confirming its role in identity preservation. 6. Conclusion and Future Work We presented Poly Track Gplus , 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. Acknowledgments Supported by the Autonomous Systems Lab and compute grants from Gplus AI Cloud. poly track gplus

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. Zhao Affiliation: Institute for Autonomous Systems & Data