The Comparative Analysis of YOLOv5/v8/v9 for Object Detection, Tracking, and Human Action Recognition in Combat Sports

Authors

DOI:

https://doi.org/10.34190/icair.4.1.3031

Keywords:

YOLOv5, YOLOv8, YOLOv9, object detection and tracking, combat sports, computer vision

Abstract

YOLO models are widely used object detectors in computer vision (CV). This study investigates the relative performance of YOLOv5, YOLOv8, and YOLOv9 for object detection, tracking, and human action recognition in combat sports. The models were evaluated using curated datasets encompassing various combat scenarios, athlete movements, and equipment configurations. Pre-processing protocols and augmentation techniques were applied to improve model accuracy and generalizability, including automated orientation correction, image dimension standardisation, contrast enhancement, and methods such as zoom, rotation, shear, and grayscale conversion. The key findings provide insight into the comparative performance of the models across various evaluation metrics, such as precision, recall, and mean average precision. Each model's ability to detect, track, and recognise human actions in dynamic combat sports environments is evaluated. Computational efficiency and real-time performance were assessed as these are important indicators for practical applications in coaching, training, and competitive scoring systems. The findings suggest that YOLOv8 offers the best balance of precision and recall, making it particularly suitable for real-time applications in combat sports analytics. This study contributes to advancing CV technologies in combat sports analytics, with potential implications for improving athletic training methods, facilitating personalised coaching interventions, and enhancing objectivity and consistency in competitive scoring processes in combat sports.

Author Biographies

Evan Quinn, The Technological University of the Shannon: Midlands Midwest

Evan Quinn is a research postgraduate student in the Information Technology Department at The Technological University of the Shannon in Limerick, Ireland. Graduated with a 2.1 Honours BSc degree in Software Development from Limerick Institute of Technology in 2020. Research interests include computer vision, artificial intelligence, data analysis, and data science.

Dr Niall Corcoran, PhD Supervisor

Dr. Niall Corcoran is a senior lecturer in information technology systems and management at the Technological University of the Shannon in Limerick, Ireland. Previously served as Head of IT Services at the University and also has considerable experience in the private sector. Research interests include information systems, knowledge management, social media and enterprise social networks.

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Published

2024-12-04