通过全局轨迹关联提升体育场景多人跟踪精度，减少身份切换。
GTA: Global Tracklet Association for Multi-Object Tracking in Sports
- 基于外观的全局轨迹关联，拆分多身份轨迹并合并同身份轨迹。
- 在SportsMOT上达81.04% HOTA，SoccerNet上提升至83.11%。
- 可作为插件工具适配任意跟踪器，适合体育视频分析应用。
体育场景中的多人跟踪已成为计算机视觉的研究热点，深度学习技术推动了显著进展。然而，仍面临玩家重新进入视野后的准确重识别以及减少身份切换等挑战。本文提出一种基于外观的全局轨迹关联算法，通过拆分包含多个身份的轨迹，并连接看似来自同一身份的轨迹，以提升跟踪性能。该方法可作为即插即用的优化模块，应用于任意多目标跟踪器以进一步提升效果。在SportsMOT数据集上，该方法达到81.04%的HOTA分数，创下新纪录；在SoccerNet数据集上，多个跟踪器的HOTA分数从79.41%提升至83.11%。跨不同跟踪器与数据集的一致性提升，验证了该方法在体育球员跟踪中的广泛应用潜力。项目代码已开源：https://github.com/sjc042/gta-link.git。
原文摘要 · Abstract (English)
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.
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