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      Footballer Action Tracking and Intervention Using Deep Learning Algorithm

      research-article

      1 , 2 , 3 , , 4

      Journal of Healthcare Engineering

      Hindawi

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          Abstract

          Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach's eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer's motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer's posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.

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          Most cited references 41

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          Realtime multi-person 2d pose estimation using part affinity fields

           Z Cao,  T. SIMON,  S.E. Wei (2021)
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            TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification

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              A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains

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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                15 March 2021
                : 2021
                Affiliations
                1School of Physical Education, Yanshan University, Qinhuangdao, Hebei 066004, China
                2Institute of Physical Education and Health, Yulin Normal University, Yulin 537000, China
                3Department of Physical Education, North China University of Science and Technology, Tangshan, Hebei 063000, China
                4School of Basic Sciences for Aviation, Naval Aviation University, Yantai, Shandong 264001, China
                Author notes

                Academic Editor: Fazlullah Khan

                Article
                10.1155/2021/5518806
                7987457
                Copyright © 2021 Guanghui Yang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: Social Science Foundation of Hebei Province Annual Project: Research on Construction of Evaluation System for Schools with Campus Football Characteristics
                Award ID: HB17TY024
                Funded by: Yulin Normal University
                Award ID: G2020SK18
                Categories
                Research Article

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