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Deep learning-based visual algorithms for identity and action recognition in engineering practical courses.

Jun Ma¹, RuoYu Wang², WenQi Lan³

¹Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China.

Scientific Reports

|March 31, 2026

Summary

This summary is machine-generated.

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This study presents a novel computer vision algorithm for recognizing student identities and actions in engineering education settings. The enhanced method improves accuracy in dynamic environments, addressing key challenges in AI-based recognition systems.

Area of Science:

Computer Vision
Artificial Intelligence
Engineering Education

Background:

Student movement in engineering education poses challenges for identity and action recognition.
Existing facial and person re-identification algorithms struggle with varying angles and accuracy.
Current action recognition methods lack practical applicability and robust training datasets.

Purpose of the Study:

To develop an advanced computer vision algorithm for accurate identity and action recognition in practical engineering courses.
To overcome limitations of current AI-based recognition systems in dynamic educational environments.

Main Methods:

An identity recognition algorithm combining facial recognition and person re-identification with dynamic feature caching.
Action recognition achieved through a target classification algorithm focusing on torso and limb recognition.

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Main Results:

The proposed algorithm demonstrated improved accuracy and effectiveness in real-world engineering course scenarios.
Comparative experimental analysis validated the algorithm's performance against existing methods.

Conclusions:

The developed algorithm offers a robust solution for identity and action recognition in dynamic educational settings.
This advancement has significant implications for AI-driven educational monitoring and analysis.