The reviewed record of science sign in
Pith

arxiv: 2405.07419 · v1 · pith:M4D637SX · submitted 2024-05-13 · cs.CR

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

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:M4D637SXrecord.jsonopen to challenge →

classification cs.CR
keywords crowdsystemlevelprojectaccuratedatasetdensitydetect
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.