{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:41:12Z","timestamp":1777488072816,"version":"3.51.4"},"reference-count":28,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018563","name":"Social Science Planning Project of Shandong Province","doi-asserted-by":"publisher","award":["19CTYJ16"],"award-info":[{"award-number":["19CTYJ16"]}],"id":[{"id":"10.13039\/501100018563","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:p>With the rapid technological advances in sports, the number of athletics increases gradually. For sports professionals, it is obligatory to oversee and explore the athletics pose in athletes\u2019 training. Key frame extraction of training videos plays a significant role to ease the analysis of sport training videos. This paper develops a sports actions\u2019 classification system for accurately classifying athlete\u2019s actions. The key video frames are extracted from the sports training video to highlight the distinct actions in sports training. Subsequently, a fully convolutional network (FCN) is used to extract the region of interest (ROI) pose detection of frames followed by the application of a convolution neural network (CNN) to estimate the pose probability of each frame. Moreover, a distinct key frame extraction approach is established to extract the key frames considering neighboring frames\u2019 probability differences. The experimental results determine that the proposed method showed better performance and can recognize the athlete\u2019s posture with an average classification rate of 98%. The experimental results and analysis validate that the proposed key frame extraction method outperforms its counterparts in key pose probability estimation and key pose extraction.<\/jats:p>","DOI":"10.1155\/2021\/1016574","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T18:05:43Z","timestamp":1630605943000},"page":"1-8","source":"Crossref","is-referenced-by-count":8,"title":["Key Frame Extraction for Sports Training Based on Improved Deep Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Changhai","family":"Lv","sequence":"first","affiliation":[{"name":"Ministry of Sports, Shandong Technology and Business University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Ministry of Sports, Shandong Technology and Business University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9535-4957","authenticated-orcid":true,"given":"Jian","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Physical Education, Henan University, Kaifeng 475001, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2012.2194993"},{"key":"2","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"E. 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