A Smartphone-Based Obstacle Sensor for the Visually Impaired

  • En Peng
  • Patrick Peursum
  • Ling Li
  • Svetha Venkatesh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6406)


In this paper, we present a real-time obstacle detection system for the mobility improvement for the visually impaired using a handheld Smartphone. Though there are many existing assistants for the visually impaired, there is not a single one that is low cost, ultra-portable, non-intrusive and able to detect the low-height objects on the floor. This paper proposes a system to detect any objects attached to the floor regardless of their height. Unlike some existing systems where only histogram or edge information is used, the proposed system combines both cues and overcomes some limitations of existing systems. The obstacles on the floor in front of the user can be reliably detected in real time using the proposed system implemented on a Smartphone. The proposed system has been tested in different types of floor conditions and a field trial on five blind participants has been conducted. The experimental results demonstrate its reliability in comparison to existing systems.


Obstacle detection visually impaired real-time monocular vision 


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  1. 1.
    Benjamin, J.M., Ali, N.A., Schepis, A.F.: A Laser Cane for the Blind. In: Proceedings of the San Diego Biomedical Symposium, vol. 12, pp. 53–57 (1973)Google Scholar
  2. 2.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  3. 3.
    Duchon, A.P., Warren, W.H., Kaelbling, L.P.: Ecological robotics. Adaptive Behavior 6(3-4), 473–507 (1998)CrossRefGoogle Scholar
  4. 4.
    Gevers, T., Smeulders, A.W.M.: Color-based object recognition. Pattern Recognition 32(3), 453–464 (1999)CrossRefGoogle Scholar
  5. 5.
    Liebelt, J., Schmid, C., Schertler, K.: Viewpoint-independent object class detection using 3D feature maps. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  6. 6.
    Plagemann, C., Endres, F., Hess, J., Stachniss, C., Burgard, W.: Monocular range sensing: a non-parametric learning approach. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA (2008)Google Scholar
  7. 7.
    Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3-D scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 30(5), 824–840 (2009)CrossRefGoogle Scholar
  8. 8.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from Internet photo collections. International Journal of Computer Vision (IJCV) 80(2), 189–210 (2008)CrossRefGoogle Scholar
  9. 9.
    Souhila, K., Karim, A.: Optical flow based robot obstacle avoidance. International Journal of Advanced Robotic Systems 4(1), 13–16 (2007)CrossRefGoogle Scholar
  10. 10.
    Sridharan, M., Stone, P.: Color learning and illumination invariance on mobile robots: a survey. Robotics and Autonomous Systems (RAS) Journal 57(6-7), 629–644 (2009)CrossRefGoogle Scholar
  11. 11.
    Tan, C., Hong, T., Chang, T., Shneier, M.: Color model-based real-time learning for road following. In: Proceedings of IEEE Intelligent Transportation Systems Conference, ITSC (2006)Google Scholar
  12. 12.
    Taylor, T., Geva, S., Boles, W.W.: Monocular vision as a range sensor. In: Proceedings of International Conference on Computational Intelligence for Modelling, CIMCA (2004)Google Scholar
  13. 13.
    Tola, E., Knorr, S., Imre, E., Alatan, A.A., Sikora, T.: Structure from motion in dynamic scenes with multiple motions. In: Workshop on Immersive Communication and Broadcast Systems, ICOB (2005)Google Scholar
  14. 14.
    Ulrich, I., Borenstein, J.: The GuideCane-applying mobile robot technologies to assist the visually impaired. IEEE Transactions on Systems, Man, and Cybernetics, Part A 31(2), 131–136 (2001)CrossRefGoogle Scholar
  15. 15.
    Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: Proceedings of the AAAI National Conference on Artificial Intelligence (2000)Google Scholar
  16. 16.
    The Association for the Blind of WA (2010),
  17. 17.
    GDP Research (2010),
  18. 18.
    Minoru 3D webcam (2010),
  19. 19.
    Currently Available Electronic Travel Aids for the Blind (2010),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • En Peng
    • 1
  • Patrick Peursum
    • 1
  • Ling Li
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Department of ComputingCurtin University of TechnologyPerthAustralia

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