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Medientyp:
E-Artikel
Titel:
Research on Athlete Behavior Recognition Technology in Sports Teaching Video Based on Deep Neural Network
Beteiligte:
Zhao, XianPin
Erschienen:
Hindawi Limited, 2022
Erschienen in:
Computational Intelligence and Neuroscience, 2022 (2022), Seite 1-13
Sprache:
Englisch
DOI:
10.1155/2022/7260894
ISSN:
1687-5273;
1687-5265
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>In recent years, due to the simple design idea and good recognition effect, deep learning method has attracted more and more researchers’ attention in computer vision tasks. Aiming at the problem of athlete behavior recognition in mass sports teaching video, this paper takes depth video as the research object and cuts the frame sequence as the input of depth neural network model, inspired by the successful application of depth neural network based on two-dimensional convolution in image detection and recognition. A depth neural network based on three-dimensional convolution is constructed to automatically learn the temporal and spatial characteristics of athletes’ behavior. The training results on UTKinect-Action3D and MSR-Action3D public datasets show that the algorithm can correctly detect athletes’ behaviors and actions and show stronger recognition ability to the algorithm compared with the images without clipping frames, which effectively improves the recognition effect of physical education teaching videos.</jats:p>