Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator

Amit Ghosh; Shamsul Arefeen Al Mahmud; Thajid Ibna Rouf Uday; Dewan Md. Farid

doi:10.1109/TENSYMP50017.2020.9230630

Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator

Amit Ghosh, Shamsul Arefeen Al Mahmud, Thajid Ibna Rouf Uday, Dewan Md. Farid

Department of Electrical Engineering and Automation

United International University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

14 Citations (Scopus)

353 Downloads (Pure)

Abstract

Assistive Technology (AT) becomes an interesting field of research in this present era. According to the World Health Organisation (WHO - https://www.who.int), there are approximately 285 million visually impaired people around the world. To address this issue, many researchers are employing new technologies, e.g. Machine Learning (ML), Computer Vision (CV), Image Processing, etc. This paper aims to develop an assistive technology based on Computer Vision, Machine Learning and Tensor Flow to support visually impaired people. The proposed system will allow the users to navigate independently using real-time object detection and identification. Hardware implementation is done to test the performance of the system, and the performance is tracked using a monitoring server. The system is developed on Raspberry pi 4 and a dedicated server with NVIDIA Titan X graphics where Google coral USB accelerator is used to boost processing power.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE Region 10 Symposium, TENSYMP 2020
Publisher	IEEE
Pages	186-189
Number of pages	4
ISBN (Electronic)	9781728173665
DOIs	https://doi.org/10.1109/TENSYMP50017.2020.9230630
Publication status	Published - 5 Jun 2020
MoE publication type	A4 Conference publication
Event	IEEE Region 10 Symposium - Virtual, Dhaka, Bangladesh Duration: 5 Jun 2020 → 7 Jun 2020

Conference

Conference	IEEE Region 10 Symposium
Abbreviated title	TENSYMP
Country/Territory	Bangladesh
City	Dhaka
Period	05/06/2020 → 07/06/2020

Keywords

Assistive Technology
Computer Vision
Object Detection
Tensor Flow
Visually Impaired

Access to Document

10.1109/TENSYMP50017.2020.9230630

ELEC_Gosh_etal_Assistive-Technology-for-Visually_TENSYMP2020_acceptedauthormanuscriptAccepted author manuscript, 2.25 MB

Cite this

@inproceedings{9c0c910efeb7427eaf432e1f31a506be,

title = "Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator",

abstract = "Assistive Technology (AT) becomes an interesting field of research in this present era. According to the World Health Organisation (WHO - https://www.who.int), there are approximately 285 million visually impaired people around the world. To address this issue, many researchers are employing new technologies, e.g. Machine Learning (ML), Computer Vision (CV), Image Processing, etc. This paper aims to develop an assistive technology based on Computer Vision, Machine Learning and Tensor Flow to support visually impaired people. The proposed system will allow the users to navigate independently using real-time object detection and identification. Hardware implementation is done to test the performance of the system, and the performance is tracked using a monitoring server. The system is developed on Raspberry pi 4 and a dedicated server with NVIDIA Titan X graphics where Google coral USB accelerator is used to boost processing power. ",

keywords = "Assistive Technology, Computer Vision, Object Detection, Tensor Flow, Visually Impaired",

author = "Amit Ghosh and {Al Mahmud}, {Shamsul Arefeen} and Uday, {Thajid Ibna Rouf} and Farid, {Dewan Md.}",

year = "2020",

month = jun,

day = "5",

doi = "10.1109/TENSYMP50017.2020.9230630",

language = "English",

pages = "186--189",

booktitle = "Proceedings of the 2020 IEEE Region 10 Symposium, TENSYMP 2020",

publisher = "IEEE",

address = "United States",

note = "IEEE Region 10 Symposium, TENSYMP ; Conference date: 05-06-2020 Through 07-06-2020",

}

Ghosh, A, Al Mahmud, SA, Uday, TIR & Farid, DM 2020, Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator. in Proceedings of the 2020 IEEE Region 10 Symposium, TENSYMP 2020., 9230630, IEEE, pp. 186-189, IEEE Region 10 Symposium, Dhaka, Bangladesh, 05/06/2020. https://doi.org/10.1109/TENSYMP50017.2020.9230630

Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator. / Ghosh, Amit; Al Mahmud, Shamsul Arefeen; Uday, Thajid Ibna Rouf et al.
Proceedings of the 2020 IEEE Region 10 Symposium, TENSYMP 2020. IEEE, 2020. p. 186-189 9230630.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Al Mahmud, Shamsul Arefeen

AU - Uday, Thajid Ibna Rouf

AU - Farid, Dewan Md.

PY - 2020/6/5

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N2 - Assistive Technology (AT) becomes an interesting field of research in this present era. According to the World Health Organisation (WHO - https://www.who.int), there are approximately 285 million visually impaired people around the world. To address this issue, many researchers are employing new technologies, e.g. Machine Learning (ML), Computer Vision (CV), Image Processing, etc. This paper aims to develop an assistive technology based on Computer Vision, Machine Learning and Tensor Flow to support visually impaired people. The proposed system will allow the users to navigate independently using real-time object detection and identification. Hardware implementation is done to test the performance of the system, and the performance is tracked using a monitoring server. The system is developed on Raspberry pi 4 and a dedicated server with NVIDIA Titan X graphics where Google coral USB accelerator is used to boost processing power.

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Assistive Technology for Visually Impaired using Tensor Flow Object Detection in Raspberry Pi and Coral USB Accelerator

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