Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes

TL;DR

This paper explores using computer vision on IP camera images within IoT environments to automate people detection, aiming to enhance security in smart cities, towns, and homes with high accuracy and minimal human oversight.

Contribution

It introduces a novel approach of analyzing image sequences as sensors in IoT, improving automation and accuracy in people detection for smart environment security.

Findings

Achieved high accuracy in detecting people using image sequence analysis

Demonstrated feasibility of computer vision as sensor in IoT environments

Reduced need for human monitoring in security systems

Abstract

Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.