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Human pose estimation based on lightweight basicblock

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Abstract

Human pose estimation based on deep learning have attracted increasing attention in the past few years and have shown superior performance on various datasets. Many researchers have increased the number of network layers to improve the accuracy of the model. However, with the deepening of the number of network layers, the parameters and computation of the model are also increasing, which makes the model unable to be deployed on edge devices and mobile terminals with limited computing power, and also makes many intelligent terminals limited in volume, power consumption and storage. Inspired by the lightweight method, we propose a human pose estimation model based on the lightweight network to solve those problems, which designs the lightweight basic block module by using the deep separable convolution and the reverse bottleneck layer to accelerate the network calculation and reduce the parameters of the overall network model. Experiments on COCO dataset and MPII dataset prove that this lightweight basicblock module can effectively reduce the amount of parameters and computation of human pose estimation model.

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Funding

This research was funded by National Natural Science Foundation of China (NSFC) under Grant No. 61771299.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Yanping Li, Ruyi Liu, Xiangyang Wang & Rui Wang

  2. Office of Academic Affairs, Shanghai University, Shanghai, China

    Yanping Li

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  1. Yanping Li
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  2. Ruyi Liu
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  3. Xiangyang Wang
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  4. Rui Wang
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Correspondence to Rui Wang.

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Li, Y., Liu, R., Wang, X. et al. Human pose estimation based on lightweight basicblock. Machine Vision and Applications 34, 3 (2023). https://doi.org/10.1007/s00138-022-01352-4

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