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Build the image acquisition model of sports athletes\u2019 running foul, divide each frame of the image samples into static area and motion area, and get the motion direction estimation results; K-means in the field of artificial intelligence is used to cluster the characteristics of sports athletes\u2019 rush foul action, and LLE algorithm is used to reduce the dimension of features; The background subtraction method is used to detect the foul target of rush, and the Bayesian algorithm is used to construct the recognition model of sports athletes\u2019 foul of rush, which is used to identify the foul target. The experimental results show that the recognition rate of this method has reached more than 72%, and continues to increase, and the recognition error is only 2%, which effectively improves the recognition rate and reduces the recognition error, which is feasible and effective.<\/jats:p>","DOI":"10.3233\/jcm-226388","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T11:29:41Z","timestamp":1662118181000},"page":"2051-2063","source":"Crossref","is-referenced-by-count":2,"title":["Application of artificial intelligence technology in recognition of sports athletes\u2019 running foul"],"prefix":"10.66113","volume":"22","author":[{"given":"Zhicheng","family":"Xie","sequence":"first","affiliation":[{"name":"School of Physical Education, Yanching Institute of Technology, Langfang, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanchang","family":"Ren","sequence":"additional","affiliation":[{"name":"Physical Education 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