TY - GEN
T1 - The Automation of Computer Vision Applications for Real-Time Combat Sports Video Analysis
AU - Quinn, Evan
AU - Corcoran, Niall
N1 - Publisher Copyright:
© 2022 Impact of Artificial Intelligence and Robotics, ECIAIR 2022
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - combat sports
KW - computer vision
KW - decision science
KW - human action recognition
KW - object detection
KW - real-time
KW - tracking
UR - http://www.scopus.com/inward/record.url?scp=85150404990&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85150404990
SN - 9781914587597
T3 - 4th European Conference on the Impact of Artificial Intelligence and Robotics, ECIAIR 2022
SP - 162
EP - 171
BT - 4th European Conference on the Impact of Artificial Intelligence and Robotics, ECIAIR 2022
PB - Academic Conferences Limited
T2 - 4th European Conference on the Impact of Artificial Intelligence and Robotics, ECIAIR 2022
Y2 - 1 December 2022 through 2 December 2022
ER -