The Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis




computer vision, real-time, human action recognition, decision science, combat sports, object detection and tracking


This study examines the potential applications of Human Action Recognition (HAR) in combat sports and aims to develop a prototype automation client that examines a video of a combat sports competition or training session and accurately classifies human movements. Computer Vision (CV) architectures that examine real-time video data streams are being investigated by integrating Deep Learning architectures into client-server systems for data storage and analysis using customised algorithms. The development of the automation client for training and deploying CV robots to watch and track specific chains of human actions is a central component of the project. Categorising specific chains of human actions allows for the comparison of multiple athletes' techniques as well as the identification of potential areas for improvement based on posture, accuracy, and other technical details, which can be used as an aid to improve athlete efficiency. The automation client will also be developed for the purpose of scoring, with a focus on the automation of the CV model to analyse and score a competition using a specific ruleset. The model will be validated by comparing performance and accuracy to that of combat sports experts. The primary research domains are CV, automation, robotics, combat sports, and decision science. Decision science is a set of quantitative techniques used to assist people to make decisions. The creation of a new automation client may contribute to the development of more efficient machine learning and CV applications in areas such as process efficiency, which improves user experience, workload management to reduce wait times, and run-time optimisation. This study found that real-time object detection and tracking can be combined with real-time pose estimation to generate performance statistics from a combat sports athlete's movements in a video.

Author Biographies

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

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, Technological University of the Shannon: Midlands Midwest, Moylish Campus, Co. Limerick, Ireland.

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, enterprise social networks, and artificial intelligence.






Masters Papers