Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU

TL;DR

Deep HM-SORT is an advanced online multi-object tracking algorithm that improves athlete tracking in sports by integrating deep features, harmonic mean, and Expansion IOU, achieving state-of-the-art results on major benchmarks.

Contribution

The paper introduces Deep HM-SORT, a novel tracking method that combines deep features, harmonic mean, and Expansion IOU to enhance accuracy and re-identification in sports scenarios.

Findings

Achieves 80.1 HOTA on SportsMOT dataset.

Achieves 85.4 HOTA on SoccerNet Tracking Challenge 2023.

Outperforms existing trackers in key metrics like IDF1 and MOTA.

Abstract

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.