轻量型多路特征融合人体姿态估计

基于深度学习的人体姿态估计广泛应用于姿态识别、人机交互等领域. 为了提升人体关键点的检测精度, 很多网络采用运算量、参数量和复杂度不断增加的模型架构, 导致无法直接部署到低算力设备. 为了解决上述问题, 本文提出了一种多路特征注意力融合的轻量型方法. 模型基于HigherHRNet网络进行轻量化设计和训练, 包括: 采用通道拆分和通道混洗, 解决分组卷积后特征层之间存在的信息隔离; 采用线性运算的特征生成方法, 解决不同特征层之间存在的冗余性; 采用融合注意力信息的方法, 缓解因轻量化导致的准确率下降. 在MS COCO数据集上完成了模型的训练、测试、可视化以及消融实验. 实验结果表明本文的轻量化方法在保证直观的检测精度前提下, 能够显著降低人体姿态估计的计算量.

Lightweight Human Pose Estimation Based on Multi-branch Feature Fusion

Human pose estimation based on deep learning is widely used in pose recognition, human-computer interaction, and other fields. In order to improve the detection accuracy of key points of the human body, many networks adopt a model architecture with increasing calculation amount, parameter amount, and complexity, which is impossible to be directly deployed to low-computing devices. To solve the above issues, this study proposes a lightweight method for multi-branch feature attention fusion. The model is based on the HigherHRNet network for lightweight design and training. Specifically, channel splitting and channel shuffling are adopted to solve the information isolation between feature layers after group convolution; the feature generation method of linear operation is used to address the redundancy between different feature layers; the method of fusing attention information is employed to alleviate the accuracy drop caused by lightweight. The training, testing, visualization, and ablation experiments of the model are completed on the MS COCO dataset. The experimental results show that the lightweight method in this study can significantly reduce the calculation amount of human pose estimation under the premise of ensuring intuitive detection accuracy.