Title: People Counting System Using OpenCV Algorithms and Edge Computing for Safety Management
Abstract: The necessity of ensuring people's safety in urban conditions during gathering in a certain indoor or outdoor area, as well as during the operation of technical equipment, requires measurement of their number, weights, the distance between them, density and many other parameters. The collection of those parameters and analysis allows you to ensure the optimal numerical value of people per area and not exceed the critical peak value, ensuring the safety of people's health and life. However, automating those processes requires stable network bandwidth and enormous computing resources for data transferring to the data center and its processing, as well as applying artificial intelligence algorithms to the data and sending the results back to the end-user. In this paper, to avoid the disadvantages of cloud computing, we consider Raspberry Pi with a camera module as an edge device to develop an automated people-counting system based on using OpenCV algorithms and Edge Computing for Safety Management. The proposed system can be used for indoor or outdoor areas, during scaffolding works and lift's operation for controlling an optimal number of people. As a result of the application the edge computing, network bandwidth and computing resources of the data center have been optimized.
Keywords: edge computing;internet of things;crowd estimation;image processing;raspberry pi;network analytics
Review score: Moderate
Review comment: The scientific article is devoted to the study of methods for creating a human counting system using OpenCV algorithms and boundary calculations for security management.
The relevance of the article is beyond doubt, since the need to ensure the safety of people in urban conditions during the gathering at a certain indoor or outdoor site, as well as during the operation of technical equipment, requires measuring their number, weight, distance between them, density and many other parameters. Automation of these processes requires stable network bandwidth and huge computing resources to transfer data to the data center and process them, as well as the application of artificial intelligence algorithms to the data and send the results back to the end user. This indicates the importance and relevance of this article.
The article submitted for review is of a scientific nature. In the course of the study, the main emphasis is placed on an automated system for counting people to ensure the safety of management using Raspberry Pi 4 Model B with an OmniVision OV 5647 camera based on computer vision technology.
The material of the article has practical and methodological substantiations of the research in the form of pictures and tables.
The article is well structured, written in a clear and understandable language, the arguments are logical and the literature corresponds to the stated topic.
The recommendation is to add about the algorithm used in your source code and add a mathematical model.
The scientific article "People Counting System Using OpenCV Algorithms and Edge Computing for Safety Management" meets the requirements of the conference. This article may be recommended for publication after corrections of comments.