Hand Held Multi-Object Tracking Dataset in American Football

TL;DR

This paper introduces the first dedicated American football player detection and tracking dataset, enabling improved evaluation and development of methods for challenging, high-density sports scenarios.

Contribution

It provides a new dataset for American football player tracking and evaluates various detection and tracking methods on this dataset.

Findings

Fine-tuning detection models improves performance.

Integrated detection and re-identification models enhance tracking accuracy.

Accurate tracking is achievable in crowded, high-occlusion scenarios.

Abstract

Multi-Object Tracking (MOT) plays a critical role in analyzing player behavior from videos, enabling performance evaluation. Current MOT methods are often evaluated using publicly available datasets. However, most of these focus on everyday scenarios such as pedestrian tracking or are tailored to specific sports, including soccer and basketball. Despite the inherent challenges of tracking players in American football, such as frequent occlusion and physical contact, no standardized dataset has been publicly available, making fair comparisons between methods difficult. To address this gap, we constructed the first dedicated detection and tracking dataset for the American football players and conducted a comparative evaluation of various detection and tracking methods. Our results demonstrate that accurate detection and tracking can be achieved even in crowded scenarios. Fine-tuning detection models improved performance over pre-trained models. Furthermore, when these fine-tuned detectors and re-identification models were integrated into tracking systems, we observed notable improvements in tracking accuracy compared to existing approaches. This work thus enables robust detection and tracking of American football players in challenging, high-density scenarios previously underserved by conventional methods.