基于HRNet的轻量化人体姿态估计网络

Lightweight Human Pose Estimation Network Based on HRNet

由于算力和内存的限制,目前的人体姿态估计网络难以广泛应用于移动设备、嵌入式平台.针对这个问题,本文以HRNet为基础框架,提出了一种轻量化的人体姿态估计网络X-HRNet,使用ResNeXt模块替换普通的Basic模块以减少网络的参数和计算复杂度.实验结果表明,所提出模型在COCO验证集上取得了78.2%的精度,比HRNet高1.9%,参数量下降了22.2M,计算量下降了27.3GFLOPs.与以往的轻量化人体姿态估计方法不同,所提出的XHRNet是一种兼顾精度和轻量化的方法,在保持精度的同时有效减少了计算量和参数量,为嵌入式平台提出了一种新的轻量化人体姿态估计网络.

The current human pose estimation networks are difficult to be widely used in mobile devices and embedded platforms due to the arithmetic power and memory limitations. To address this problem, this paper proposes a lightweight human pose estimation network X-HRNet with HRNet as the basic framework and uses the ResNeXt module to replace the common basic module to reduce the parameters and computational complexity of the network.The proposed model achieves 78.2% accuracy on the COCO validation set, which is 1.9% higher than that of the HRNet, the number of parameters decreases by 22.2M, and the computational effort decreases by 27.3 GFLOPs.The proposed X-HRNet is a method with the combination accuracy and lightweight, which proposes a new lightweight human pose estimation network for embedded platforms by reducing the computation and the number of parameters effectively while maintaining accuracy.