用领域特征提升体育比赛中长时间球员追踪准确率
Towards long-term player tracking with graph hierarchies and domain-specific features
- 构建分层图模型，融合球衣号、队伍标识等专用信息
- 在足球与冰球数据集上实现高精度长期追踪
- 适合体育分析、计算机视觉方向的研究者参考
团队运动分析中，由于球员外观相似、遮挡和动态运动模式，长期球员追踪仍具挑战性。在视线外或长时间遮挡后准确重识别球员并重新连接轨迹对稳健分析至关重要。我们提出SportsSUSHI，一种基于分层图的方法，利用球衣号、队伍ID和场地坐标等领域特定特征提升追踪精度。该方法在SoccerNet数据集和新提出的冰球追踪数据集上表现优异。我们的冰球数据集由固定相机拍摄整个场地，包含长序列及队伍标识和球衣号标注，适合评估长期追踪能力。实验表明，引入领域特征显著提升了关联准确性。代码与数据集已公开于https://github.com/mkoshkina/sports-SUSHI。
原文摘要 · Abstract (English)
In team sports analytics, long-term player tracking remains a challenging task due to player appearance similarity, occlusion, and dynamic motion patterns. Accurately re-identifying players and reconnecting tracklets after extended absences from the field of view or prolonged occlusions is crucial for robust analysis. We introduce SportsSUSHI, a hierarchical graph-based approach that leverages domain-specific features, including jersey numbers, team IDs, and field coordinates, to enhance tracking accuracy. SportsSUSHI achieves high performance on the SoccerNet dataset and a newly proposed hockey tracking dataset. Our hockey dataset, recorded using a stationary camera capturing the entire playing surface, contains long sequences and annotations for team IDs and jersey numbers, making it well-suited for evaluating long-term tracking capabilities. The inclusion of domain-specific features in our approach significantly improves association accuracy, as demonstrated in our experiments. The dataset and code are available at https://github.com/mkoshkina/sports-SUSHI.
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