Real Time Obstacle Detection for Visual Impaired Individuals / Narbe Christian O. Concepcion and Meryll Mikka A. Magdale.
Material type:
TextPublisher: Rosario, Cavite : Cavite State University-CCAT Campus, 2019Description: xii, 36 leaves : illustrations ; 28 cmSubject(s): Detectors -- Design and construction | Image processing -- Digital techniques | Image analysis | Visually impaired | Dense pixelLOC classification: UM TA 1637 | C66 2019Summary: CONCEPCION, NARBE CHRISTIAN O., MAGDALE, MERYLL MIKKA A. Real Time Obstacle Detection for Visual Impaired Individuals. Design Project. Department of Engineering. Cavite State University-Cavite College of Arts and Trades Campus, Rosario, Cavite. January 2019. Adviser: Engr. Kenneth J. Enrico, Technical Critic: Allen Paul K. Aclan.
The study was conducted on November 2018 to develop a real time obstacle detection for blind persons to assist in their navigation.
1) design and develop the hardware of the device that is more light weight and portable to use for the user; 2) create a program that can detect obstacles that block the way of visually impaired person and alert them using micro-computer, 3) evaluate the technical performance of the device in terms of its accuracy and response time in detecting object; and 4) assessment of the design using an 1SO instrument for product quality. We used the optical flow algorithm as image processing — an optical flow used to compute the motion of the pixel of an image sequence. It provides a dense (point to point) pixel correspondence.
The camera is used as a means of the observer to monitor if there is an object ahead. The system will automatically start when the code is uploaded on the Raspberry Pi 3 Model B+ and when the camera captures an image detected as an object, the system will release a beeping sound and will continue until there are blocking objects.
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Cavite State University - CCAT Campus | Thesis/Manuscript/Dissertation | TH | UM TA 1637 C66 2019 (Browse shelf) | 1 copy | Available | T0005199 |
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| UM TA 1637 A24 2018 Facial Recognition System for Car Ignition using OpenCV, DLIB Framework and Raspberry Pi 3 Model B / | UM TA 1637 A24 2018 Facial Recognition System for Car Ignition using OpenCV, DLIB Framework and Raspberry Pi 3 Model B / | UM TA 1637 A43 2019 Coffee Berry Grader and Ripeness Detector / | UM TA 1637 C66 2019 Real Time Obstacle Detection for Visual Impaired Individuals / | UM TA 1637 G83 2018 Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons / | UM TA 1637 M88 2019 MediLeaf : a Herbal Plant Classification System using Image Processing / | UM TA165 D45 2018 Development of Electronics Mousetrap / |
Design Project (BSCpE)--Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
CONCEPCION, NARBE CHRISTIAN O., MAGDALE, MERYLL MIKKA A. Real Time Obstacle Detection for Visual Impaired Individuals. Design Project. Department of Engineering. Cavite State University-Cavite College of Arts and Trades Campus, Rosario, Cavite. January 2019. Adviser: Engr. Kenneth J. Enrico, Technical Critic: Allen Paul K. Aclan.
The study was conducted on November 2018 to develop a real time obstacle detection for blind persons to assist in their navigation.
1) design and develop the hardware of the device that is more light weight and portable to use for the user; 2) create a program that can detect obstacles that block the way of visually impaired person and alert them using micro-computer, 3) evaluate the technical performance of the device in terms of its accuracy and response time in detecting object; and 4) assessment of the design using an 1SO instrument for product quality. We used the optical flow algorithm as image processing — an optical flow used to compute the motion of the pixel of an image sequence. It provides a dense (point to point) pixel correspondence.
The camera is used as a means of the observer to monitor if there is an object ahead. The system will automatically start when the code is uploaded on the Raspberry Pi 3 Model B+ and when the camera captures an image detected as an object, the system will release a beeping sound and will continue until there are blocking objects.
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