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Escalator Safety System using Computer Vision and Deep Learning

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dc.contributor.advisor Tamimi, Hashem
dc.contributor.author Salhab, Hala
dc.contributor.author Salhab, Mohammad
dc.contributor.author Khatib, Amjad
dc.date.accessioned 2025-05-07T10:02:37Z
dc.date.available 2025-05-07T10:02:37Z
dc.date.issued 2024-12-01
dc.identifier.uri scholar.ppu.edu/handle/123456789/9206
dc.description no of pages 77, علم حاسوب 5/2024
dc.description.abstract Escalators are essential components of modern infrastructure, facilitating vertical transportation in public spaces. However, escalator-related accidents pose a persistent safety challenge. This project introduces an escalator safety system that utilizes computer vision and deep learning to enhance hazard detection and response mechanisms. The proposed system, built on an ESP32 and Raspberry Pi, employs a pre-trained deep learning action recognition mode, namely the LRCN to analyze video footage for identifying risks, such as people falling on the escalator or becoming jammed at key points. The results showed that the system has high accuracy of nearly 94.99% . The results also showed that the LRCN model required high computational time. Although the simulation on a Personal computer was done in real time, we did not reach a real time response on the Raspberry Pi microcontroller en_US
dc.language.iso en en_US
dc.publisher جامعة بوليتكنك فلسطين - علم حاسوب en_US
dc.subject Escalator en_US
dc.title Escalator Safety System using Computer Vision and Deep Learning en_US
dc.type Other en_US


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