FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction

Abdelrahman Mostafa, Muhammad Rushdi, Muhammad Rushdi, Tamer Basha, Khaled Sayed

2023

Abstract

Data analytics have had a significant impact on tactical and workload planning in football. Football data is divided into two categories: event data, which captures on-the-ball events like passes and shots, and tracking data, which captures off-the-ball movements. However, traditional methods of collecting tracking data are expensive and inconvenient. Recently, AI solutions have emerged as low-cost and user-friendly alternatives to track players’ movements from video streams. This paper introduces FOOTBALLTrace, an end-to-end AI system for tracking multiple football players from a panoramic game view. The system also incorporates a novel algorithm that detects potential occlusion events and ensures trajectory continuity for occluded players. The workflow involves five stages: panoramic view creation, player detection, player ID association, occlusion detection, and trajectory correction. The system utilizes YOLOv7 for multiple object detection and employs a pre-trained deep affinity network to assign unique IDs to players throughout the game. Occlusion detection and trajectory correction are achieved by extracting geometric features from discontinuous player trajectories. The system’s performance was evaluated on full-length video data of a football game, with occlusion events manually extracted for training and testing the occlusion detection and trajectory correction algorithm. The system achieved an 87.5% trajectory correction rate for occluded trajectories.

Paper Citation

in Harvard Style

Mostafa A., Rushdi M., Basha T. and Sayed K. (2023). FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction. In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS; ISBN 978-989-758-673-6, SciTePress, pages 194-201. DOI: 10.5220/0012226800003587

in Bibtex Style

@conference{icsports23,
author={Abdelrahman Mostafa and Muhammad Rushdi and Tamer Basha and Khaled Sayed},
title={FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction},
booktitle={Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS},
year={2023},
pages={194-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012226800003587},
isbn={978-989-758-673-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS
TI - FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction
SN - 978-989-758-673-6
AU - Mostafa A.
AU - Rushdi M.
AU - Basha T.
AU - Sayed K.
PY - 2023
SP - 194
EP - 201
DO - 10.5220/0012226800003587
PB - SciTePress