Yong In University, Yongin 17092, Republic of Korea.
Honam University, Gwangju 62399, Republic of Korea.
Comput Intell Neurosci. 2022 May 26;2022:5625006. doi: 10.1155/2022/5625006. eCollection 2022.
In sports, because the movement of the human body is composed of the movements of the human limbs, and the complex and changeable movements of the human limbs lead to various and complicated movement modes of the entire human body, it is not easy to accurately track the human body movement. The recognition of human characteristic behavior belongs to a higher level computer vision topic, which is used to understand and describe the characteristic behavior of people, and there are also many research difficulties. Because the radial basis fuzzy neural network has the characteristics of parallel processing, nonlinearity, fault tolerance, self-adaptation, and self-learning, it has the advantage of high recognition efficiency when it is applied to the recognition of intersecting features and incomplete features. Therefore, this paper applies it to the analysis of the human body information recognition model in sports. The research results show that the human body information recognition model proposed in this paper has a high recognition accuracy and can detect the movement state of people in sports in real time and accurately.
在体育领域，由于人体的运动是由人体四肢的运动组成的，而人体四肢的复杂多变的运动导致了整个人体的各种复杂的运动模式，因此不容易准确地跟踪人体运动。人体特征行为的识别属于更高层次的计算机视觉课题，用于理解和描述人的特征行为，也有许多研究难点。由于径向基模糊神经网络具有并行处理、非线性、容错性、自适应和自学习等特点，因此在应用于交叉特征和不完整特征的识别时具有较高的识别效率。因此，本文将其应用于体育中人体信息识别模型的分析。研究结果表明，本文提出的人体信息识别模型具有较高的识别精度，能够实时、准确地检测体育中人们的运动状态。
