Lightweight high-performance pose recognition network: HR-LiteNet

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 Mo...

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Bibliographic Details
Published in:Electronic research archive 2024, Vol.32 (2), p.1145-1159
Main Authors: Cai, Zhiming, Zhuang, Liping, Chen, Jin, Jiang, Jinhua
Format: Article
Language:English
Subjects:
ghost
high-resolution network
human pose estimation
lightweight module
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Summary: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.
ISSN:2688-1594
2688-1594
DOI:10.3934/era.2024055