College of Sport, Xuchang University, Xuchang 461000, Henan, China.
Comput Intell Neurosci. 2022 Jun 28;2022:5277157. doi: 10.1155/2022/5277157. eCollection 2022.
The application of sports game video analysis in athlete training and competition analysis feedback has attracted extensive attention, but the traditional sports human body posture estimation method has a large error between the athlete's human body posture estimation results and the actual results in the complex environment and the athlete's body parts are blocked. Therefore, this study proposes a convolutional neural network for athlete pose estimation in sports game video. Based on the improved model, multiscale model, and large perception model, a superimposed hourglass network is constructed, and the gradient disappearance problem of the convolutional neural network is solved using intermediate supervision. The experimental results show that the athlete pose estimation model based on the convolutional neural network can improve the accuracy of athlete pose estimation and reduce the negative impact of occlusion environment on athlete pose estimation to a certain extent. In addition, compared with other athletes' standing posture estimation methods, the model has competitive advantages and high accuracy under widely used standard conditions.
运动视频分析在运动员训练和比赛分析反馈中的应用引起了广泛关注，但是传统的运动人体姿态估计方法在复杂环境和运动员身体部位被遮挡的情况下，运动员人体姿态估计结果与实际结果之间存在较大误差。因此，本研究提出了一种用于运动视频中运动员姿态估计的卷积神经网络。基于改进的模型、多尺度模型和大感受野模型，构建了一个叠加沙漏网络，并使用中间监督解决了卷积神经网络的梯度消失问题。实验结果表明，基于卷积神经网络的运动员姿态估计模型可以提高运动员姿态估计的准确性，并在一定程度上降低遮挡环境对运动员姿态估计的负面影响。此外，与其他运动员站立姿态估计方法相比，该模型在广泛使用的标准条件下具有竞争优势和高精度。
