This paper introduces Deep HM-SORT, a novel online multi-object tracking
algorithm specifically designed to enhance the tracking of athletes in sports
scenarios. Traditional multi-object tracking methods often struggle with sports
environments due to the similar appearances of players, irregular and
unpredictable movements, and significant camera motion. Deep HM-SORT addresses
these challenges by integrating deep features, harmonic mean, and Expansion
IOU. By leveraging the harmonic mean, our method effectively balances
appearance and motion cues, significantly reducing ID-swaps. Additionally, our
approach retains all tracklets indefinitely, improving the re-identification of
players who leave and re-enter the frame. Experimental results demonstrate that
Deep HM-SORT achieves state-of-the-art performance on two large-scale public
benchmarks, SportsMOT and SoccerNet Tracking Challenge 2023. Specifically, our
method achieves 80.1 HOTA on the SportsMOT dataset and 85.4 HOTA on the
SoccerNet-Tracking dataset, outperforming existing trackers in key metrics such
as HOTA, IDF1, AssA, and MOTA. This robust solution provides enhanced accuracy
and reliability for automated sports analytics, offering significant
improvements over previous methods without introducing additional computational
cost.

该论文介绍了一种名为 Deep HM-SORT 的新型在线多目标跟踪算法，专门用于提高运动场景中运动员的跟踪。通过集成深度特征、谐波平均和 Expansion IOU，Deep HM-SORT 解决了传统多目标跟踪方法在体育环境中面临的挑战，有效平衡外观和动作线索，显著降低 ID 交换，并改进了离开和重进画面的运动员的再识别问题。实验结果表明，Deep HM-SORT 在两个大规模公共基准测试中取得了最先进的性能，分别在 SportsMOT 数据集上达到 80.1 HOTA 和在 SoccerNet-Tracking 数据集上达到 85.4 HOTA，在 HOTA、IDF1、AssA 和 MOTA 等关键指标上胜过现有的跟踪器。该稳健的解决方案为自动化体育分析提供了提高准确性和可靠性的显著改进，而且没有引入额外的计算成本。