论文标题
基于改进的堆叠沙漏网络的单个上肢姿势估计方法
Single upper limb pose estimation method based on improved stacked hourglass network
论文作者
论文摘要
目前，由于网络模型的复杂结构，大多数高准确的单人姿势估计方法具有很高的计算复杂性和不足的实时性能。但是，由于网络模型的简单结构，具有高实时性能的单人姿势估计方法也需要提高其准确性。目前，在单人姿势估计中很难同时实现高精度和实时性能。为了用于人机合作操作，本文提出了一种基于端到端方法的单人上肢姿势估计方法，以进行准确和实时的肢体姿势估计。使用堆叠的沙漏网络模型，设计了一个单人上肢骨架键检测模型。采用二次卷积来替换原始模型中沙漏模块的上采样操作，以解决粗糙特征图的问题。积分回归用于计算骨骼关键点的位置坐标，从而减少了量化误差和计算。实验表明，开发的单人物上肢骨骼钥匙点检测模型可实现高精度，并且基于端到端方法的姿势估计方法可提供高精度和实时性能。
At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model was designed.Deconvolution was employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression was used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments showed that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.
