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

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Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition.

Zhenyue Qin, Yang Liu, Pan Ji

IEEE Transactions on Neural Networks and Learning Systems

|September 19, 2022

Summary

This study introduces Angular Encoding (AGE) to improve skeleton-based action recognition, enhancing accuracy for complex human movements on edge devices by capturing higher-order joint relationships.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Skeleton sequences are efficient for action recognition on edge devices.
Current methods use spatial-temporal cues from joint coordinates in graph neural networks.
Existing models struggle with actions having similar motion trajectories.

Purpose of the Study:

To improve the robustness and accuracy of skeleton-based action recognition.
To address limitations of current models in distinguishing similar actions.
To introduce a novel feature representation for human action analysis.

Main Methods:

Proposed fusing higher-order features using Angular Encoding (AGE).
Integrated AGE into popular spatial-temporal graph neural network architectures.
Evaluated the approach on NTU60 and NTU120 benchmarks.

Main Results:

Achieved new state-of-the-art accuracy on NTU60 and NTU120 datasets.
Demonstrated robust capture of joint and body part relationships.
The proposed method requires fewer parameters and reduced run time.

Conclusions:

Angular Encoding (AGE) effectively enhances skeleton-based action recognition.
The method offers improved performance and efficiency for edge devices.
This approach advances the field of human action recognition.