Mobile indoor navigation with object recognition for visually impaired
Navigation is an important aspect in daily life, but visually impaired individuals might struggle to navigate by themselves safely and independently. Nowadays, the advancement of mobile solutions with artificial intelligence (AI) and computer vision (CV) technology has encouraged the developme...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7102/1/fyp_CS_2025_GYS.pdf http://eprints.utar.edu.my/7102/ |
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| _version_ | 1854094473750904832 |
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| author | Gooi, Yong Shen |
| author_facet | Gooi, Yong Shen |
| author_sort | Gooi, Yong Shen |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Navigation is an important aspect in daily life, but visually impaired individuals might struggle
to navigate by themselves safely and independently. Nowadays, the advancement of mobile
solutions with artificial intelligence (AI) and computer vision (CV) technology has encouraged
the development of many innovative solutions to solve problems. In this project, a standalone
mobile application called Visiovigate is developed for indoor navigation assistance of the
visually impaired communities. It is designed to support visually impaired individuals by
integrating real-time object recognition, and indoor navigation using mobile sensors. Existing
assistive technologies often lack comprehensive indoor navigation abilities or are reliant on
expensive hardware. It might limit their accessibility and effectiveness. Thus, Visiovigate
addresses these gaps by leveraging deep learning and computer vision techniques by using the
You Only Look Once (YOLO) model for efficient object detection on mobile devices and
utilizing the mobile pedestrian dead reckoning mobile sensors like magnetometer and
accelerometer for indoor navigation without relying on global positioning system (GPS) and
internet connection. The application will also offer real-time audio and haptic feedback for
ensuring the visually impaired users receive immediate environmental awareness and
directional guidance. Therefore, this mobile application is best to use with a traditional solution
like cane that will further increase efficiency as this application can inform users about the
incoming obstacles that are not reachable by the traditional solution. This system operates
entirely on standard mobile sensors which have been commonly built into smartphones
nowadays. It aims to run in a stable condition at any mobile device without internet connection.
Hence, this project also will provide a more cost-effective and accessible solution that enhances
the safety and independence of its users in indoor environments. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7102 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.71022025-12-28T15:55:20Z Mobile indoor navigation with object recognition for visually impaired Gooi, Yong Shen T Technology (General) Navigation is an important aspect in daily life, but visually impaired individuals might struggle to navigate by themselves safely and independently. Nowadays, the advancement of mobile solutions with artificial intelligence (AI) and computer vision (CV) technology has encouraged the development of many innovative solutions to solve problems. In this project, a standalone mobile application called Visiovigate is developed for indoor navigation assistance of the visually impaired communities. It is designed to support visually impaired individuals by integrating real-time object recognition, and indoor navigation using mobile sensors. Existing assistive technologies often lack comprehensive indoor navigation abilities or are reliant on expensive hardware. It might limit their accessibility and effectiveness. Thus, Visiovigate addresses these gaps by leveraging deep learning and computer vision techniques by using the You Only Look Once (YOLO) model for efficient object detection on mobile devices and utilizing the mobile pedestrian dead reckoning mobile sensors like magnetometer and accelerometer for indoor navigation without relying on global positioning system (GPS) and internet connection. The application will also offer real-time audio and haptic feedback for ensuring the visually impaired users receive immediate environmental awareness and directional guidance. Therefore, this mobile application is best to use with a traditional solution like cane that will further increase efficiency as this application can inform users about the incoming obstacles that are not reachable by the traditional solution. This system operates entirely on standard mobile sensors which have been commonly built into smartphones nowadays. It aims to run in a stable condition at any mobile device without internet connection. Hence, this project also will provide a more cost-effective and accessible solution that enhances the safety and independence of its users in indoor environments. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7102/1/fyp_CS_2025_GYS.pdf Gooi, Yong Shen (2025) Mobile indoor navigation with object recognition for visually impaired. Final Year Project, UTAR. http://eprints.utar.edu.my/7102/ |
| spellingShingle | T Technology (General) Gooi, Yong Shen Mobile indoor navigation with object recognition for visually impaired |
| title | Mobile indoor navigation with object recognition for visually impaired |
| title_full | Mobile indoor navigation with object recognition for visually impaired |
| title_fullStr | Mobile indoor navigation with object recognition for visually impaired |
| title_full_unstemmed | Mobile indoor navigation with object recognition for visually impaired |
| title_short | Mobile indoor navigation with object recognition for visually impaired |
| title_sort | mobile indoor navigation with object recognition for visually impaired |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7102/1/fyp_CS_2025_GYS.pdf http://eprints.utar.edu.my/7102/ |
| url_provider | http://eprints.utar.edu.my |