Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as \emph{SportsMOT}, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as \emph{MixSort}, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT. The dataset and code will be available at https://deeperaction.github.io/datasets/sportsmot.html.

翻译：多目标跟踪技术在体育场景中对于收集运动员统计数据、支持进一步分析（如自动战术分析）具有关键作用。然而，现有MOT基准测试对该领域的关注甚少，制约了相关技术的发展。本文提出了一个面向多样体育场景的大规模多目标跟踪数据集（命名为SportsMOT），旨在对赛场上的所有运动员进行跟踪。该数据集包含240个视频序列、超过15万帧图像（约为MOT17的15倍）及超过160万个边界框（约为MOT17的3倍），涵盖篮球、排球和足球三类运动。本数据集具有两个关键特性：1）快速且变速的运动特征；2）相似但可区分的表观特征。我们期望SportsMOT能够推动MOT跟踪器在运动关联与表观关联方面的技术提升。通过对多个前沿跟踪器进行基准测试，我们发现SportsMOT的核心挑战在于目标关联问题。为缓解该问题，我们进一步提出了一种新的多目标跟踪框架MixSort，将类似MixFormer的结构作为辅助关联模型引入主流的检测-跟踪范式。通过将定制化的表观关联与原始运动关联相结合，MixSort在SportsMOT和MOT17上均达到了领先水平。基于MixSort，我们对SportsMOT进行了深入分析并提出了富有洞察力的见解。数据集与代码将开源至https://deeperaction.github.io/datasets/sportsmot.html。