# GTA: Global Tracklet Association for Multi-Object Tracking in Sports

arXiv: https://arxiv.org/abs/2411.08216 · HTML: https://mlsanity.com/paper/2411.08216/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2403.10826/) (2024)
- [Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information](https://mlsanity.com/paper/2301.07805/) (2023)

## Built on by (most cited first, 10 of 10)

- [SoccerNet 2025 Challenges Results](https://mlsanity.com/paper/2508.19182/) (2025)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Selective Mask Propagation for Multi-Object Tracking](https://mlsanity.com/paper/2606.13033/) (2026)
- [Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking](https://mlsanity.com/paper/2505.18111/) (2025)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [GTATrack: Winner Solution to SoccerTrack 2025 with Deep-EIoU and Global Tracklet Association](https://mlsanity.com/paper/2602.00484/) (2026)
- [Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video](https://mlsanity.com/paper/2603.04864/) (2026)
- [Beyond Pedestrians: Caption-Guided CLIP Framework for High-Difficulty Video-based Person Re-Identification](https://mlsanity.com/paper/2604.07740/) (2026)
- [Training-Free Long-Term Multi-Object Tracking for Sports Video Analytics](https://mlsanity.com/paper/2608.15688/) (2026)
- [ARC-SORT: Adaptive, Robust, Compensated Multi-Object Tracking with Interacting Multiple Models](https://mlsanity.com/paper/2610.22306/) (2026)

## Abstract (arXiv)

Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, challenges remain, such as accurately re-identifying players upon re-entry into the scene and minimizing ID switches. In this paper, we propose an appearance-based global tracklet association algorithm designed to enhance tracking performance by splitting tracklets containing multiple identities and connecting tracklets seemingly from the same identity. This method can serve as a plug-and-play refinement tool for any multi-object tracker to further boost their performance. The proposed method achieved a new state-of-the-art performance on the SportsMOT dataset with HOTA score of 81.04%. Similarly, on the SoccerNet dataset, our method enhanced multiple trackers' performance, consistently increasing the HOTA score from 79.41% to 83.11%. These significant and consistent improvements across different trackers and datasets underscore our proposed method's potential impact on the application of sports player tracking. We open-source our project codebase at https://github.com/sjc042/gta-link.git.