Stereovision System for Visually Impaired

  • Rafal Kozik
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


Nowadays the problem of BVIPs (Blind and Visually Impaired Persons) social exclusion arises to one of the major problems of modern society. It is usually framed in terms of accessibility to services like shops, theaters or cafeterias. The disability is main barrier both for fully and partially blinded to become an active members of society. However, thanks to growing progress in computer vision together with increasing power of portable devices new opportunities and solutions appear. Nearly real-time vision-based algorithms and knowledge-based systems start to help visually impaired during daily activities increasing social inclusion. New solutions for people with vision impairment are dedicated to support the user during the decision process, giving the information about obstacles located in the environment. However, in many cases this information is still not enough for blind person to have full situational awareness. Therefore the solution presented in this paper engages the stereo camera and image processing algorithms to facilitate its user with object detection and recognition mechanisms. The risk assessment based on ontology problem modeling allows to handle the risk, predict possible user’s moves and provide the user with appropriate set of suggestions that will eliminate or reduce the discovered risk.


Stereo Match Blind Person Depth Discontinuity Stereovision System Surf Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pelcztnski, P.: Travel Aid System for the Blind. Image Processing and Communications Challenges, 324–333 (2009)Google Scholar
  2. 2.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions via graph cuts, In: I, pp. II: 508-515 (2001)Google Scholar
  3. 3.
    Birchfield, S., Tomasi, C.: Depth Discontinuities by Pixel-to-Pixel Stereo. International Journal of Computer Vision 35(3), 269–293 (1999)CrossRefGoogle Scholar
  4. 4.
    Torralba, Fergus, R., Weiss, Y.: Small codes and large databases for recognition. In: CVPR (2008)Google Scholar
  5. 5.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: CVPR (2008)Google Scholar
  6. 6.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. In: CVPR, pp. 1: 261–1: 268 (2004)Google Scholar
  7. 7.
    Yang, X., Tian, Y.: Robust Door Detection in Unfamiliar Environments by Combining Edge and Corner FeaturesGoogle Scholar
  8. 8.
    Chen, Z., Birchfield, S.: Visual Detection of Lintel-Occluded Doors from a Single Image. In: IEEE Computer Society Workshop on Visual Localization for Mobile Platforms (in association with CVPR), Anchorage, Alaska (June 2008)Google Scholar
  9. 9.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Wang, S., Wang, H.: 2D staircase detection using real adaboost. In: Proceedings of the 7th International Conference on Information, Communications and Signal Processing, Macau, China, pp. 376–380. IEEE Press, Los Alamitos (2009), ISBN: 978-1-4244-4656-8Google Scholar
  11. 11.
    MoBIC: an aid to increase the independent mobility of blind and elderly travellers. 2nd TIDE Congress, Paris, La Villette, April 26 - 28 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rafal Kozik
    • 1
  1. 1.Institute of TelecommunicationsUniversity of Technology & Life SciencesBydgoszczPoland

Personalised recommendations