  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.
