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Multi-Level Feature Fusion in CNN-Based Human Action Recognition: A Case Study on EfficientNet-B7.

Pitiwat Lueangwitchajaroen¹, Sitapa Watcharapinchai¹, Worawit Tepsan²

¹National Electronic and Computer Technology Center, National Science and Technology Development Agency, Khlong Luang, Pathum Thani 12120, Thailand.

Journal of Imaging

|December 27, 2024

Summary

This summary is machine-generated.

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This study introduces a novel multi-level fusion approach for human action recognition using only RGB frames. The method significantly improves accuracy by integrating information at various stages, outperforming single-modality models.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Human action recognition is crucial for applications like healthcare and autonomous driving.
Existing methods often rely on multiple data modalities and late fusion techniques.
Collecting diverse data types in real-world scenarios presents challenges.

Purpose of the Study:

To develop a multi-level fusion approach for human action recognition.
To leverage multimodal techniques using only RGB frames as a single data source.
To enhance model performance by combining information at early, intermediate, and late stages.

Main Methods:

Utilized RGB frames from the NTU RGB+D dataset.
Extracted 2D skeleton coordinates and optical flow frames from RGB data using pre-trained models.

Keywords:

fusion method human action recognition multi-level fusion

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Implemented a multi-level fusion strategy combining information across different stages.

Main Results:

Achieved 91.5% accuracy on the NTU RGB+D 60 dataset.
Demonstrated significant improvements over single-modality and single-view models.
The proposed approach showed comparable performance to state-of-the-art methods.

Conclusions:

Multi-level fusion of features extracted from RGB frames is effective for human action recognition.
The approach offers a practical solution by relying on a single data source.
This method provides a robust and efficient alternative to existing techniques.