Liu Jiatian
College of Strength and Conditioning, Beijing Sport University, Beijing, China.
Front Psychol. 2021 Jun 25;12:663359. doi: 10.3389/fpsyg.2021.663359. eCollection 2021.
In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.
为了分析运动员的运动心理并识别运动员在运动中的心理状态，本研究设计了一种人体动作识别（HAR）算法。首先，基于卷积神经网络（CNN）建立HAR模型，通过分析收集视频中任务的动作信息来对当前动作状态进行分类。其次，结合相关运动心理学理论，研究篮球运动员在攻防过程中表现出假动作时的心理。然后，还通过收集的视频分析运动员的心理，以便预测运动员的下一个反应动作。实验结果表明，灰度图像与红绿蓝（RGB）图像相结合可以减少图像损失，并有效提高模型的识别准确率。本研究设计的优化卷积三维网络（C3D）HAR模型的识别准确率为80%，图像损失为5.6。此外，时间复杂度降低了33%。因此，所提出的优化C3D能够有效识别人类动作，本研究结果可为体育领域人体动作图像识别研究提供参考。
