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.

翻译：体育场景下的多目标跟踪已成为计算机视觉领域的研究热点之一，借助深度学习技术的融合取得了显著进展。尽管已有突破性成果，但仍存在诸多挑战，例如运动员重新进入场景时的准确重识别以及身份切换的最小化。本文提出一种基于外观特征的全局轨迹段关联算法，旨在通过拆分包含多重身份的轨迹段并连接看似属于同一身份的轨迹段来提升跟踪性能。该方法可作为任意多目标跟踪器的即插即用式优化工具，以进一步提升其性能。所提方法在SportsMOT数据集上取得了81.04%的HOTA分数，创造了新的最优性能记录。在SoccerNet数据集上，本方法同样提升了多种跟踪器的性能，将HOTA分数从79.41%持续提高至83.11%。这些在不同跟踪器与数据集上取得的显著且一致的改进，凸显了所提方法在体育运动员跟踪应用中的潜在影响力。我们在https://github.com/sjc042/gta-link.git开源了项目代码库。