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Design and Implementation of Football Player Training Management System Based on Intelligent Image
Author(s) -
Yang Sun,
Changjun Hu
Publication year - 2022
Publication title -
applied bionics and biomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.397
H-Index - 23
eISSN - 1754-2103
pISSN - 1176-2322
DOI - 10.1155/2022/6091557
Subject(s) - football , informatization , computer science , management system , training system , multimedia , realization (probability) , training (meteorology) , plan (archaeology) , human–computer interaction , simulation , real time computing , embedded system , engineering , world wide web , operations management , statistics , physics , mathematics , archaeology , political science , economic growth , law , economics , history , meteorology
This article is aimed at studying the design and implementation of a football player training management system based on smart images. Based on the analysis of the importance of informatization for scientific football training, system performance requirements and intelligent image detection technology, the football player training management is designed. The overall architecture of the system, and the detailed design of each functional module of the system. It mainly includes football player information management module, football player training plan viewing module, training goal formulation module and training information feedback module. The realization of the training management system relies on intelligent image technology to detect and track athletes. Finally, the performance of the system was tested. The test results show that the expected response time of each module of the system when different numbers of users are accessed is within 3 seconds. The longest actual time is 2.64 s, and the actual shortest time is 1.18 s. It can be seen that the response time of the system meets the demand. At the same time, the system throughput rate meets the requirements of this article, and the user pass rate is also above 95%, indicating that the performance of the football player training management system designed in this article is better.

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