Skip to main content
Springer Nature Link
Log in

Development of a wearable guide device based on convolutional neural network for blind or visually impaired persons

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This study proposes a design for a wearable guide device for blind or visually impaired persons on the basis of video streaming and deep learning. This work mainly aims to provide supplementary assistance to white canes used by visually impaired persons and offer them increased freedom of movement and independence using the proposed wearable device. The considerable amount of environmental information provided by the device also ensures enhanced safety for its users. Computer vision in the proposed device uses an RGB camera instead of the RGBD camera commonly used in computer vision. Deep learning is applied to convert RGB images into depth images and calculate the plane for detecting indoor objects and safe walking routes. A convolutional neural network (CNN) is adopted, and its neural network structure, which is similar to that of the human brain, simulates a neural transmission mechanism similar to that triggered in human learning. Therefore, this system can learn a large number of feature routes and then generate a model from the learning result. The proposed system can help blind or visually impaired persons identify flat and safe walking routes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Achar S, Bartels JR, Whittaker WLR, Kutulakos KN, Narasimhan SG (2017) Epipolar time-of-flight imaging. ACM Trans Graph 36(4):37:1–37:8

    Article  Google Scholar 

  2. Azenkot S, Feng C, Cakmak M (2016) Enabling building service robots to guide blind people a participatory design approach. In: 2016 11th ACM/IEEE international conference on human-robot interaction (HRI), pp 3–10

  3. Bai J, Lian S, Liu Z, Wang K, Liu D (2018) Virtual-blind-road following-based wearable navigation device for blind people. IEEE Trans Consum Electron 64(1):136–143

    Article  Google Scholar 

  4. Baig MH, Jagadeesh V, Piramuthu R, Bhardwaj A, Di W, Sundaresan N (2014) Im2depth: scalable exemplar based depth transfer. In: IEEE Winter conference on applications of computer vision, pp 145–152

  5. Caltagirone L, Scheidegger S, Svensson L, Wahde M (2017) Fast lidar-based road detection using fully convolutional neural networks. In: 2017 IEEE intelligent vehicles symposium (IV), pp 1019–1024

  6. Chin LC, Basah SN, Yaacob S, Din MY, Juan YE (2015) Accuracy and reliability of optimum distance for high performance kinect sensor. In: 2015 2nd international conference on biomedical engineering (ICoBE), pp 1–7

  7. Diamantas S, Astaras S, Pnevmatikakis A (2016) Depth estimation in still images and videos using a motionless monocular camera. In: 2016 IEEE international conference on imaging systems and techniques (IST), pp 129–134

  8. Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. In: Proceedings of the 27th international conference on neural information processing systems, vol 2, pp 2366–2374

  9. Fabrizio F, Luca AD (2017) Real-time computation of distance to dynamic obstacles with multiple depth sensors. IEEE Robot Autom Lett 2(1):56–63

    Article  MathSciNet  Google Scholar 

  10. Fernandes LA, Oliveira MM (2008) Real-time line detection through an improved hough transform voting scheme. Pattern Recognit 41(1):299–314

    Article  MATH  Google Scholar 

  11. Forouher D, Besselmann MG, Maehle E (2016) Sensor fusion of depth camera and ultrasound data for obstacle detection and robot navigation. In: 2016 14th international conference on control, automation, robotics and vision (ICARCV), pp 1–6

  12. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

  13. Hoiem D, Efros AA, Hebert M (2005) Automatic photo pop-up. ACM Trans Graph 24(3):577–584

    Article  Google Scholar 

  14. Islam MA, Bruce N, Wang Y (2016) Dense image labeling using deep convolutional neural networks. In: 2016 13th Conference on computer and robot vision (CRV), pp 16–23

  15. Islam MM, Sadi MS, Zamli KZ, Ahmed MM (2019) Developing walking assistants for visually impaired people: a review. IEEE Sens J 19 (8):2814–2828

    Article  Google Scholar 

  16. Jin Y, Li J, Ma D, Guo X, Yu H (2017) A semi-automatic annotation technology for traffic scene image labeling based on deep learning preprocessing. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), pp 315–320

  17. Karsch K, Liu C, Kang SB (2014) Depth transfer: depth extraction from video using non-parametric sampling. IEEE Trans Pattern Anal Mach Intell 36 (11):2144–2158

    Article  Google Scholar 

  18. Khoshelham K (2011) Accuracy analysis of kinect depth data. In: International archives of the photogrammetry, remote sensing and spatial information sciences, pp 133–138

  19. Kuznietsov Y, Stückler J, Leibe B (2017) Semi-supervised deep learning for monocular depth map prediction. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2215–2223

  20. Lee HS, Lee KM (2013) Simultaneous super-resolution of depth and images using a single camera. In: 2013 IEEE conference on computer vision and pattern recognition, pp 281–288

  21. Liaquat S, Khan US, Ata-Ur-Rehman (2015) Object detection and depth estimation of real world objects using single camera. In: 2015 Fourth international conference on aerospace science and engineering (ICASE), pp 1–4

  22. Liu F, Shen C, Lin G, Reid I (2016) Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans Pattern Anal Mach Intell 38(10):2024–2039

    Article  Google Scholar 

  23. Liu S, Yu M, Li M, Xu Q (2019) The research of virtual face based on deep convolutional generative adversarial networks using tensorflow. Phys A: Stat Mech Appl 521:667–680

    Article  Google Scholar 

  24. Liu S, Li M, Li M, Xu Q (2020) Research of animals image semantic segmentation based on deep learning. Concurr Comput: Pract Exp 31 (1):e4892

    Google Scholar 

  25. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3431–3440

  26. Maurer M (2012) White cane safety day: a symbol of independence. National Federation of the Blind

  27. Michels J, Saxena A, Ng AY (2005) High speed obstacle avoidance using monocular vision and reinforcement learning. In: Proceedings of the 22nd international conference on machine learning, pp 593–600

  28. Naseer T, Burgard W (2017) Deep regression for monocular camera-based 6-dof global localization in outdoor environments. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1525–1530

  29. Saxena A, Chung SH, Ng AY (2005) Learning depth from single monocular images. In: Proceedings of the 18th international conference on neural information processing systems, pp 1161–1168

  30. Saxena A, Sun M, Ng AY (2009) Make3d: learning 3d scene structure from a single still image. IEEE Trans Pattern Anal Mach Intell 31(5):824–840

    Article  Google Scholar 

  31. Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Proceedings of the 12th European conference on computer vision—volume part V, pp 746–760

  32. Sokic E, Ferizbegovic M, Zubaca J, Softic K, Ahic-Djokic M (2015) Design of ultrasound-based sensory system for environment inspection robots. In: 2015 57th international symposium ELMAR (ELMAR), pp 141–144

  33. Stejskal M, Mrva J, Faigl J (2016) Road following with blind crawling robot. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 3612–3617

  34. Straub J, Freifeld O, Rosman G, Leonard JJ, Fisher JW (2018) The manhattan frame model—manhattan world inference in the space of surface normals. IEEE Trans Pattern Anal Mach Intell 40(1):235–249

    Article  Google Scholar 

  35. Tian H, Zhuang B, Hua Y, Cai A (2014) Depth inference with convolutional neural network. In: 2014 IEEE visual communications and image processing conference, pp 169–172

  36. Toha SF, Yusof HM, Razali MF, Halim AHA (2015) Intelligent path guidance robot for blind person assistance. In: 2015 International conference on informatics, electronics vision (ICIEV), pp 1–5

  37. Štrbac M, Marković M, Popović DB (2012) Kinect in neurorehabilitation: computer vision system for real time hand and object detection and distance estimation. In: 11th Symposium on neural network applications in electrical engineering, pp 127–132

  38. Xu Q (2013) A novel machine learning strategy based on two-dimensional numerical models in financial engineering. Math Probl Eng 2013:1–6

    Google Scholar 

  39. Xu Q, Li M (2019) A new cluster computing technique for social media data analysis. Clust Comput 22:2731–2738

    Article  Google Scholar 

  40. Xu Q, Wu J, Chen Q (2014) A novel mobile personalized recommended method based on money flow model for stock exchange. Math Probl Eng 2014:1–9

    MathSciNet  MATH  Google Scholar 

  41. Xu Q, Li M, Li M, Liu S (2018a) Energy spectrum ct image detection based dimensionality reduction with phase congruency. J Med Syst 42(49):1–14

  42. Xu Q, Wang Z, Wang F, Li J (2018b) Thermal comfort research on human ct data modeling. Multimed Tools Appl 77(5):6311–6326

  43. Xu Q, Li M, Yu M (2019a) Learning to rank with relational graph and pointwise constraint for cross-modal retrieval. Soft Comput 23:9413–9427

  44. Xu Q, Wang F, Gong Y, Wang Z, Zeng K, Li Q, Luo X (2019b) A novel edge-oriented framework for saliency detection enhancement. Image Vis Comput 87:1–12

  45. Xu Q, Wang Z, Wang F, Gong Y (2019c) Multi-feature fusion cnns for drosophila embryo of interest detection. Phys A: Stat Mech Appl 531:121808

  46. Xu Q, Huang G, Yu M, Guo Y (2020) Fall prediction based on key points of human bones. Phys A: Stat Mech Appl 540:123205

    Article  MathSciNet  Google Scholar 

  47. Yin LS, Sheng YK, Soetedjo A (2008) Developing a blind robot: study on 2d mapping. In: 2008 IEEE conference on innovative technologies in intelligent systems and industrial applications, pp 12–14

  48. žbontar J, LeCun Y (2016) Stereo matching by training a convolutional neural network to compare image patches. J Mach Learn Res 17(1):2287–2318

    MATH  Google Scholar 

  49. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6230–6239

Download references

Acknowledgements

This research was supported in part by the Ministry of Science and Technology (contracts MOST-108-2221-E-019-038-MY2, MOST-107-2221-E-019-039-MY2, MOST-109-2634-F-008-007, and MOST-109-2634-F-019-001) of Taiwan. This research was also funded by the University System of Taipei Joint Research Program (contract USTP-NTUT-NTOU-109-01), Taiwan.

Author information

Authors and Affiliations

  1. National Taiwan Ocean University, Keelung, Taiwan

    Yi-Zeng Hsieh, Shih-Syun Lin & Fu-Xiong Xu

Authors
  1. Yi-Zeng Hsieh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shih-Syun Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fu-Xiong Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shih-Syun Lin.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsieh, YZ., Lin, SS. & Xu, FX. Development of a wearable guide device based on convolutional neural network for blind or visually impaired persons. Multimed Tools Appl 79, 29473–29491 (2020). https://doi.org/10.1007/s11042-020-09464-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09464-7

Keywords