Department of Physical Education, Lvliang University, Shanxi Lvliang 033001, China.
Comput Intell Neurosci. 2021 Dec 28;2021:4367875. doi: 10.1155/2021/4367875. eCollection 2021.
In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a large perceptual field residual module is designed to learn the correlation between the athlete components in the sports game video within a large perceptual field. A multiscale residual module is designed in the paper to better solve the inaccuracy of the pose estimation due to the problem of scale change of the athlete components in the sports game video. Secondly, these three residual modules are used as the building blocks of the convolutional neural network. When the resolution is high, the large perceptual field residual module and the multiscale residual module are used to capture information in a larger range as well as at each scale, and when the resolution is low, only the improved residual module is used. Finally, four multiresidual module convolutional neural networks are used to form the final multiresidual module stacked convolutional neural network. The neural network model proposed in this paper achieves high accuracy of 89.5% and 88.2% on the upper arm and lower arm, respectively, so the method in this paper reduces the influence of occlusion on the athlete's posture estimation to a certain extent. Through the experiments, it can be seen that the proposed multiresidual module stacked convolutional neural network-based method for athlete pose estimation in sports game videos further improves the accuracy of athlete pose estimation in sports game videos.
在本文中，我们提出了一种基于多残差模块卷积神经网络的方法，用于体育比赛视频中的运动员姿势估计。该网络首先设计了一种基于传统残差模块的改进残差模块。首先，设计了一个大感受野残差模块，以学习体育比赛视频中运动员成分之间在大感受野内的相关性。本文设计了一个多尺度残差模块，以更好地解决由于体育比赛视频中运动员成分的尺度变化问题导致的姿势估计不准确的问题。其次，这三个残差模块作为卷积神经网络的构建块。当分辨率较高时，使用大感受野残差模块和多尺度残差模块在较大范围内以及每个尺度上捕获信息，而当分辨率较低时，仅使用改进的残差模块。最后，使用四个多残差模块卷积神经网络来形成最终的多残差模块堆叠卷积神经网络。本文提出的神经网络模型在上臂和小臂上分别实现了 89.5%和 88.2%的高精度，因此本文的方法在一定程度上降低了遮挡对运动员姿势估计的影响。通过实验可以看出，所提出的基于多残差模块堆叠卷积神经网络的体育比赛视频中运动员姿势估计方法进一步提高了体育比赛视频中运动员姿势估计的准确性。
