Indoor and Outdoor Crowd Density Level Estimation with Video Analysis through Machine Learning Models

TL;DR

This paper presents a machine learning-based system for estimating crowd density from images and videos, aiding in crowd safety management with high accuracy and user-friendly design.

Contribution

The authors developed a cost-effective AI system capable of detecting, tracking, and estimating crowd levels from visual data with over 97% accuracy.

Findings

Achieved over 97% accuracy in crowd density estimation.

Developed a user-friendly and low-cost AI system.

Visualized dataset through graphical representations.

Abstract

Crowd density level estimation is an essential aspect of crowd safety since it helps to identify areas of probable overcrowding and required conditions. Nowadays, AI systems can help in various sectors. Here for safety purposes or many for public service crowd detection, tracking or estimating crowd level is essential. So we decided to build an AI project to fulfil the purpose. This project can detect crowds from images, videos, or webcams. From these images, videos, or webcams, this system can detect, track and identify humans. This system also can estimate the crowd level. Though this project is simple, it is very effective, user-friendly, and less costly. Also, we trained our system with a dataset. So our system also can predict the crowd. Though the AI system is not a hundred percent accurate, this project is more than 97 percent accurate. We also represent the dataset in a graphical way.