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Related Experiment Video

Updated: Jul 5, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Human Pose Estimation Based on Efficient and Lightweight High-Resolution Network (EL-HRNet).

Rui Li^1,2, An Yan¹, Shiqiang Yang¹

¹School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

An Efficient and Lightweight High-Resolution Network (EL-HRNet) was developed for computer vision human pose estimation. This model reduces parameters and computational cost while maintaining high accuracy on benchmark datasets.

Keywords:

CBAM HRNet human pose estimation lightweight network

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Human pose estimation is a key area in computer vision.
High-Resolution Network (HRNet) is a classical method but computationally expensive.
Deployment of complex models is challenging on resource-limited devices.

Purpose of the Study:

To propose an improved, efficient, and lightweight HRNet (EL-HRNet) for human pose estimation.
To reduce model complexity and computational requirements.
To maintain high accuracy in human pose estimation.

Main Methods:

Developed lightweight residual modules using point-wise and grouped convolutions.
Integrated the Convolutional Block Attention Module (CBAM) to form the Lightweight Attention Basicblock (LA-Basicblock).
Evaluated the model on COCO2017 and MPII datasets.

Main Results:

The EL-HRNet model has 5 million parameters and 2.0 GFlops.
Achieved an AP score of 67.1% on the COCO2017 validation set.
Obtained a PCKh@0.5mean of 87.7% on the MPII validation set.

Conclusions:

The EL-HRNet model offers a significant reduction in parameters and computational cost.
The proposed model demonstrates a strong balance between efficiency and accuracy in human pose estimation.
EL-HRNet is suitable for deployment on resource-constrained platforms.