Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
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arXiv
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| Format: | Preprint |
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2024
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| author | Yozgatli, Pinar Acar, Yavuz Tulumen, Mehmet Minga, Selman Selamet, Salih Nalbant, Beytullah Toru, Mustafa Talha Koca, Berna Keles, Tevfik Selcok, Mehmet |
| author_facet | Yozgatli, Pinar Acar, Yavuz Tulumen, Mehmet Minga, Selman Selamet, Salih Nalbant, Beytullah Toru, Mustafa Talha Koca, Berna Keles, Tevfik Selcok, Mehmet |
| contents | Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model Yozgatli, Pinar Acar, Yavuz Tulumen, Mehmet Minga, Selman Selamet, Salih Nalbant, Beytullah Toru, Mustafa Talha Koca, Berna Keles, Tevfik Selcok, Mehmet Computer Vision and Pattern Recognition Artificial Intelligence Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention. |
| title | Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01985 |