Papers › Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic...
Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU
Matias Gran-Henriksen, Hans Andreas Lindgaard, Gabriel Kiss, Frank Lindseth
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Object Tracking | SportsMOT | Deep HM-SORT | AssA | 72.7 | #4 of 22 | Archive leaderboard | report |
| Multi-Object Tracking | SportsMOT | Deep HM-SORT | DetA | 88.3 | #4 of 22 | Archive leaderboard | report |
| Multi-Object Tracking | SportsMOT | Deep HM-SORT | HOTA | 80.1 | #4 of 22 | Archive leaderboard | report |
| Multi-Object Tracking | SportsMOT | Deep HM-SORT | IDF1 | 85.2 | #4 of 22 | Archive leaderboard | report |
| Multi-Object Tracking | SportsMOT | Deep HM-SORT | MOTA | 96.6 | #4 of 22 | Archive leaderboard | report |
| Multiple Object Tracking | SportsMOT | Deep HM-SORT | AssA | 72.7 | #2 of 19 | Archive leaderboard | report |
| Multiple Object Tracking | SportsMOT | Deep HM-SORT | DetA | 88.3 | #2 of 19 | Archive leaderboard | report |
| Multiple Object Tracking | SportsMOT | Deep HM-SORT | HOTA | 80.1 | #2 of 19 | Archive leaderboard | report |
| Multiple Object Tracking | SportsMOT | Deep HM-SORT | IDF1 | 85.2 | #2 of 19 | Archive leaderboard | report |
| Multiple Object Tracking | SportsMOT | Deep HM-SORT | MOTA | 96.6 | #2 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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