Affiliations: [a] College of Physical Education, Hubei Engineering University, Hubei, China
| [b] College of Physical Education, Hubei University of Arts and Science, Hubei, China
Correspondence:
[*]
Corresponding author. Yuliang Ji, Hubei University of Arts and Science, Hubei, 441053, China.
E-mail: [email protected].
Abstract: The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.
