Multi-object tracking (MOT) in sports is highly challenging due to irregular player motion, uniform appearances, and frequent occlusions. These difficulties are further exacerbated by the geometric distortion and extreme scale variation introduced by static fisheye cameras. In this work, we present GTATrack, a hierarchical tracking framework that win first place in the SoccerTrack Challenge 2025. GTATrack integrates two core components: Deep Expansion IoU (Deep-EIoU) for motion-agnostic online association and Global Tracklet Association (GTA) for trajectory-level refinement. This two-stage design enables both robust short-term matching and long-term identity consistency. Additionally, a pseudo-labeling strategy is used to boost detector recall on small and distorted targets. The synergy between local association and global reasoning effectively addresses identity switches, occlusions, and tracking fragmentation. Our method achieved a winning HOTA score of 0.60 and significantly reduced false positives to 982, demonstrating state-of-the-art accuracy in fisheye-based soccer tracking. Our code is available at https://github.com/ron941/GTATrack-STC2025.

翻译：体育场景中的多目标跟踪因运动员运动轨迹不规则、外观相似度高以及频繁遮挡而极具挑战性。静态鱼眼镜头带来的几何畸变与极端尺度变化进一步加剧了这些困难。本研究提出GTATrack——一种分层式跟踪框架，该方案在SoccerTrack Challenge 2025竞赛中获得冠军。GTATrack整合了两个核心组件：用于运动无关在线关联的深度扩展交并比（Deep-EIoU）模块，以及用于轨迹级优化的全局轨迹关联（GTA）模块。这种两阶段设计既能实现鲁棒的短期匹配，又能保持长期身份一致性。此外，我们采用伪标签策略提升检测器对畸变小目标的召回率。局部关联与全局推理的协同作用有效解决了身份切换、遮挡及轨迹断裂等问题。本方法以0.60的HOTA得分夺冠，并将误报数显著降低至982例，在鱼眼足球跟踪领域达到了最先进的精度水平。代码已开源：https://github.com/ron941/GTATrack-STC2025。