Analyzing the feature extraction of football player's offense action using machine vision, big data, and internet of things.

These days, the integration of big data and information systems enables industries such as sports, health, and medical sciences in resource optimization by enhancing their operational efficiency. Personalized health management, driven by advancements in big data analysis, feature extraction, and internet of things (IoT) has emerged as a prominent trend in the sports industry. Based on these advancements, this paper focuses on leveraging machine vision, big data, and IoT to conduct a comprehensive study for extracting football players' offense features by aiming to improve accuracy and ensure the players' safety in sports industry. First, this paper discusses a framework for sports health, which is based on big data analysis. Next, we discuss the use of big data and feature extraction for IoT and sports health informatics. The action image of an offense for football players is collected by an intelligent camera, and the gathered imaging code is read and analyzed by radio frequency technology. Using the recognition method of offense action using deep learning and motion information, the offense action's image features and the three-color features of video RGB are fused into deep learning features to complete the offense action recognition. Based on the offense action recognition, the Harris 3D operator is applied to construct the sequence potential function of the offense action, and the sequence possible function of the offense action is used as the operational technique for this purpose. In addition, the AdaBoost algorithm is used to comprehensively screen the characterized data of athletes' offense actions, and the filtered data are used as training samples to complete the feature extraction of athletes' offense actions. The experimental results show that the proposed method can effectively reduce the recognition error of offense action and improve the efficiency of action's feature extraction and the accuracy of feature extraction. [ABSTRACT FROM AUTHOR]