@article{Cai2024, 
author = {Zhiming Cai and Liping Zhuang and Jin Chen and Jinhua Jiang},
title = {Lightweight high-performance pose recognition network: HR-LiteNet},
year = {2024},
journal = {Electronic Research Archive},
volume = {32},
number = {2},
pages = {1145-1159},
keywords = {human pose estimation, high-resolution network, Ghost, lightweight module},
url = {https://www.sciopen.com/article/10.3934/era.2024055},
doi = {10.3934/era.2024055},
abstract = {To address the limited resources of mobile devices and embedded platforms, we propose a lightweight pose recognition network named HR-LiteNet. Built upon a high-resolution architecture, the network incorporates depthwise separable convolutions, Ghost modules, and the Convolutional Block Attention Module to construct L_block and L_basic modules, aiming to reduce network parameters and computational complexity while maintaining high accuracy. Experimental results demonstrate that on the MPII validation dataset, HR-LiteNet achieves an accuracy of 83.643% while reducing the parameter count by approximately 26.58 M and lowering computational complexity by 8.04 GFLOPs compared to the HRNet network. Moreover, HR-LiteNet outperforms other lightweight models in terms of parameter count and computational requirements while maintaining high accuracy. This design provides a novel solution for pose recognition in resource-constrained environments, striking a balance between accuracy and lightweight demands.}
}