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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07419 |
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| _version_ | 1866910444182044672 |
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| author | Arefin, Mahira Wadud, Md. Anwar Hussen Rahman, Anichur |
| author_facet | Arefin, Mahira Wadud, Md. Anwar Hussen Rahman, Anichur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07419 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Indoor and Outdoor Crowd Density Level Estimation with Video Analysis through Machine Learning Models Arefin, Mahira Wadud, Md. Anwar Hussen Rahman, Anichur Cryptography and Security 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. |
| title | Indoor and Outdoor Crowd Density Level Estimation with Video Analysis through Machine Learning Models |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2405.07419 |