Implementation of Recognition System of People and Computers in a Classroom Through Artificial Vision

Abstract

This work encompasses the implementation of an autonomous system that recognizes people and object in a classroom to later develop a smart classroom. This system uses artificial intelligent, artificial vision and deep learning technics to process images, sent by a camera that was previously installed in the class. The information is processed through a YOLO-type convolutional neural network, which is open source and is already trained and validated for real-time object detection applications, this information will contribute to the creation of an intelligent classroom. The aim of the system is getting the number of people and laptops that are inside a defined work area. The smart classroom goal is to influence the student’s behavior to: improve their academy performance, cognitive skills, and most importantly create a dynamic and interactive learning environment where the relationship between classmates and teacher improves.