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Related Experiment Video

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Published on: December 15, 2023

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context.

Lorenzo Monti¹, Rita Tse², Su-Kit Tang²

¹INAF-Istituto di Radioastronomia, 40127 Bologna, Italy.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This study introduces a camera and Raspberry Pi-based system for accurate indoor people counting. The edge-based transfer learning approach enables deployment in diverse environments without retraining, achieving a low error rate.

Keywords:

Internet of Things ambient intelligence deep learning occupancy detection smart buildings smart environments smart sensing transfer learning

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Area of Science:

Computer Vision
Artificial Intelligence
Ubiquitous Computing

Background:

Increased focus on indoor people monitoring for safety and resource management, heightened by the COVID-19 pandemic.
Need for adaptable and privacy-preserving people counting systems in various indoor settings.

Purpose of the Study:

To propose and evaluate an edge-based people counting system using cameras and Raspberry Pi.
To develop a framework adaptable to different indoor environments without requiring specific retraining.
To ensure the system is scalable and compliant with privacy regulations.

Main Methods:

Utilized cameras and Raspberry Pi platforms for data acquisition and processing.
Implemented an edge-based transfer learning framework with specialized image processing techniques.
Deployed and tested the system in diverse university campus classrooms.

Main Results:

The proposed system demonstrated feasibility across different classroom types.
Achieved a maximum mean absolute error of 1.23 for people counting.
The architecture supports scalability and privacy compliance.

Conclusions:

The camera and Raspberry Pi-based edge computing system offers a feasible solution for indoor people counting.
The transfer learning approach allows for broad applicability across varied indoor environments.
The system is scalable, privacy-compliant, and achieves high accuracy.