Decision support system for effective action recognition of track and field sports using Ant Colony Optimization

Abstract

Abstract Based on emerging technologies like artificial intelligence, machine learning, the Internet of Things, and virtual reality, various Decision Support Systems (DSS) are being employed for the revolution in the sports industry. The coach can now make very precise and unbiased decisions related to the players’ skills and selection. It is now very convenient to improve the skills and performance of the players through the implementation of various computer-grounded methodologies. Professionals can recognize the unwanted behavior of players in time during sports and hence can ensure a peaceful atmosphere during sports. The recognition of non-standard actions by the players can help in the avoidance of serious injuries or illness. The DSS can predict the nature of the weather and the sports personnel can take decisions regarding the carrying out of games. The players can do their training without any restrictions on space and time. The real-time analysis of already existing videos of games can help the newcomers learn and improve their skills and performance. The trainers can check the physical fitness of the sportsmen very efficiently and provide them with useful and valuable recommendations related to their fitness level. The proposed study has used the Ant Colony Optimization to recognize and track the optimal features of athletes to enhance individual as well as team performance in sport competitions.