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An Electronic Guide Dog for the Blind Based on Artificial Neural Networks

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HCI International 2021 - Late Breaking Posters (HCII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1499))

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Abstract

This paper presents a feasibility study of an electronic assistance system to support blind and visually impaired people in finding their way in the area of public traffic. Optical recognition of walkways is implemented. For this purpose, a neural network for semantic segmentation is trained from scratch. In the practical test, an NVIDIA® Jetson NanoTM is used as the computing unit. A voice output gives the user feedback for orientation on the pavement.

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References

  1. Bhole, S., Dhok, A.: Deep learning based object detection and recognition framework for the visually-impaired. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 725–728. IEEE (2020)

    Google Scholar 

  2. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2017). arXiv:1606.00915

  3. CoinCheung: CoinCheung/BiSeNet. https://github.com/CoinCheung/BiSeNet, original-date: 2018–11-29T04:27:51Z

  4. Elmannai, W., Elleithy, K.: Sensor-based assistive devices for visually-impaired people: current status, challenges, and future directions. Sensors 17(3), 565 (2017). https://doi.org/10.3390/s17030565, http://www.mdpi.com/1424-8220/17/3/565

  5. Jie, X., Xiaochi, W., Zhigang, F.: Research and implementation of blind sidewalk detection in portable eta system. In: 2010 International Forum on Information Technology and Applications, vol. 2, pp. 431–434 (2010). https://doi.org/10.1109/IFITA.2010.187

  6. Lopatin, S.: Electronic Guide Dog - video of some tests. https://youtu.be/B0bz_o08990

  7. Saaid, M.F., Ismail, I., Noor, M.Z.H.: Radio frequency identification walking stick (rfiws): A device for the blind. In: 2009 5th International Colloquium on Signal Processing & its Applications, pp. 250–253. IEEE (2009)

    Google Scholar 

  8. Sheth, R., Rajandekar, S., Laddha, S., Chaudhari, R.: Smart white cane-an elegant and economic walking aid. Am. J. Eng. Res. 3(10), 84–89 (2014)

    Google Scholar 

  9. Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: BiSeNet v2: bilateral network with guided aggregation for real-time semantic segmentation (2020). arXiv:2004.02147

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Authors and Affiliations

  1. Technische Hochschule Mittelhessen, Institute of Technology and Computer Science, University of Applied Sciences, Giessen, Germany

    Sergej Lopatin, Florian von Zabiensky, Michael Kreutzer, Klaus Rinn & Diethelm Bienhaus

Authors
  1. Sergej Lopatin
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  2. Florian von Zabiensky
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  3. Michael Kreutzer
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  4. Klaus Rinn
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  5. Diethelm Bienhaus
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Corresponding author

Correspondence to Sergej Lopatin .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Lopatin, S., von Zabiensky, F., Kreutzer, M., Rinn, K., Bienhaus, D. (2021). An Electronic Guide Dog for the Blind Based on Artificial Neural Networks. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-90179-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90178-3

  • Online ISBN: 978-3-030-90179-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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