Markerless Localization for Blind Users Using Computer Vision and Particle Swarm Optimization

  • Hashem Tamimi
  • Anas Sharabati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)


In this paper, we propose a novel approach, which aims to solve the localization and target-finding problem for blind and partially sighted people. A guidance system, solely implemented on a mobile phone with a camera, is employed. A computer vision approach integrated with Particle Swarm Optimization (PSO) is proposed for tracking the location. Using PSO leads to many advantages: first, it is possible to obtain robust localization results by combining the current and historical information about the location of the blind person. Second, it helps the system to resolve from ambiguous situations caused by facing similar images at different locations. Third, it can detect and recover from cases where the calculated location is wrong. Experimental results show that the proposed method works efficiently because of the simplicity of the approach, which makes it suitable for mobile applications.


Particle Swarm Optimization Mobile Phone Mobile Robot Color Histogram Scale Invariant Feature Transform 
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.


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  1. 1.
    Aherne, F., Thacker, N., Rockett, P.I.: The bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 4(4), 363–368 (1998)MathSciNetGoogle Scholar
  2. 2.
    Brabyn, J.: Technology as a support system for orientation and mobility. The Free Library, September 22 (1997)Google Scholar
  3. 3.
    Chiu, D., O’Keefe, K.: Seamless outdoor-to-indoor positioning. GPS World 20(3), 32–38 (2009)Google Scholar
  4. 4.
    Coroama, V., Röthenbacher, F.: The chatty environment - providing everyday independence to the visually impaired. In: Workshop on ubiquitous computing for pervasive healthcare applications at UbiComp 2003, Seattle (October 2003)Google Scholar
  5. 5.
    Coughlan, J., Manduchi, R.: Functional assessment of a camera phone-based wayfinding system operated by blind and visually impaired users. International Journal on Artificial Intelligence Tools 18(3), 379–397 (2009)CrossRefGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Nagoya (1995)CrossRefGoogle Scholar
  8. 8.
    Hub, A., Diepstraten, J., Ertl, T.: Augmented indoor modeling for navigation support for the blind. In: The International Conference on Computers for People with Special Needs (CPSN 2005), Las Vegas, pp. 54–62 (2005)Google Scholar
  9. 9.
    Hub, A., Hartter, T., Ertl, T.: Interactive tracking of movable objects for the blind on the basis of environment models and perception-oriented object recognition methods. In: Assets 2006: Proceedings of the 8th international ACM SIGACCESS conference on computers and accessibility, pp. 111–118. ACM, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Kronfeld, M., Weiß, C., Zell, A.: A dynamic swarm for visual location tracking. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 203–210. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV), pp. 96–102. IEEE Computer Society Press, Washington (1996)Google Scholar
  13. 13.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1(1), 33–57 (2007)CrossRefGoogle Scholar
  14. 14.
    Sonnoqrot, F., Younis, E., Tamimi, H.: Vision-based localization aid for the blind. In: Palestinian International Conference on Computer and Information Technology (PICCIT), Hebron, Palestine, September 1-3 (2007)Google Scholar
  15. 15.
    Tamimi, H.: Vision-based features for mobile robot localization. Ph.D. thesis, University of Tüebingen. Tuebingen, Germany (2006)Google Scholar
  16. 16.
    Tamimi, H., Andreasson, H., Treptow, A., Duckett, T., Zell, A.: Localization of mobile robots with omnidirectional vision using particle filter and iterative sift. In: Proceedings of the European Conference on Mobile Robots (ECMR), Ancona, Italy, pp. 1–7 (2005)Google Scholar
  17. 17.
    Tamimi, H., Halawani, A., Burkhardt, H., Zell, A.: Appearance-based localization of mobile robots using local integral invariants. In: Proceedings of the International Conference on Intelligent Autonomous Systems (IAS-9), Tokyo, Japan (March 2006)Google Scholar
  18. 18.
    Treuillet, S., Royer, E., Chateau, T., Dhome, M., Lavest, J.: Body mounted vision system for visually impaired outdoor and indoor wayfinding assistance. In: Proceedings of the Conference and Workshop on Assistive Technologies for People with Vision and Hearing Impairments: Assistive Technology for All Ages (CVHI 2007), Granada, Spain, August 28-31 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hashem Tamimi
    • 1
  • Anas Sharabati
    • 1
  1. 1.Information Technology Department, College of Administrative Sciences and InformaticsPalestine Polytechnic UniversityHebron

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