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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Published on: October 27, 2016

An efficient and accurate 2D human pose estimation method using VTTransPose network.

Rui Li^1,2, Qi Li¹, Shiqiang Yang³

¹School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xian, 710000, China.

Scientific Reports

|March 31, 2024

Summary

This summary is machine-generated.

The VTTransPose model enhances human pose estimation by improving efficiency and accuracy. This transformer-based approach offers better performance with reduced computational demands and memory usage.

Keywords:

Feature fusion Human pose estimation Transformer Twin attention

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Area of Science:

Computer Vision
Machine Learning

Background:

Transformer-based models are popular for human pose estimation.
Existing models face challenges with high computational costs and local detail recognition.

Purpose of the Study:

To improve the efficiency and accuracy of transformer-based human pose estimation.
To address limitations in feature representation and network performance.

Main Methods:

Introduced the Twin attention module in TransPose for efficiency.
Developed VTTransPose by replacing the basic block with an intra-level feature fusion V block.
Validated performance on COCO val2017 and COCO test-dev2017 datasets.

Main Results:

VTTransPose achieved AP scores of 76.5 on COCO val2017 and 73.6 on COCO test-dev2017.
Demonstrated improvements of 0.4 and 0.2 AP over the original TransPose.
Reduced computational demands by 4.8G FLOPs, 2M parameters, and ~40% memory usage.

Conclusions:

VTTransPose offers superior accuracy and efficiency compared to the original TransPose model.
The model is more lightweight and suitable for resource-constrained environments.