GTA: Global Tracklet Association for Multi-Object Tracking in Sports

TL;DR

This paper introduces a global tracklet association algorithm that improves multi-object tracking in sports by re-identifying players and reducing ID switches, achieving state-of-the-art results on key datasets.

Contribution

The paper presents a novel appearance-based tracklet association method that enhances existing multi-object trackers in sports scenarios, serving as a plug-and-play refinement tool.

Findings

Achieved a new state-of-the-art HOTA score of 81.04% on SportsMOT.

Improved HOTA scores from 79.41% to 83.11% on SoccerNet across multiple trackers.

Demonstrated consistent performance improvements across different datasets and trackers.

Abstract

Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, challenges remain, such as accurately re-identifying players upon re-entry into the scene and minimizing ID switches. In this paper, we propose an appearance-based global tracklet association algorithm designed to enhance tracking performance by splitting tracklets containing multiple identities and connecting tracklets seemingly from the same identity. This method can serve as a plug-and-play refinement tool for any multi-object tracker to further boost their performance. The proposed method achieved a new state-of-the-art performance on the SportsMOT dataset with HOTA score of 81.04%. Similarly, on the SoccerNet dataset, our method enhanced multiple trackers' performance, consistently increasing the HOTA score from 79.41% to 83.11%. These significant and consistent improvements across different trackers and datasets underscore our proposed method's potential impact on the application of sports player tracking. We open-source our project codebase at https://github.com/sjc042/gta-link.git.