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
this https URL

本文提出了一种新的大规模多目标追踪数据集 SportsMOT，旨在促进运动跟踪器在运动和外观等方面的进步，通过比较当下几种状态最好的跟踪器和 MixSort 框架，我们发现 SportsMOT 的关键挑战在于对象关联，MixSort 框架通过将自定义的基于外观的关联与原始的基于运动的关联相结合，实现了在 SportsMOT 和 MOT17 上的最先进的性能表现。