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Hand Gesture Recognition Using Time-of-Flight Camera and Viewpoint Feature Histogram

  • Tomasz Kapuściński
  • Mariusz Oszust
  • Marian Wysocki
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 230)

Abstract

Time-of-flight (ToF) cameras acquire 3D information about observed scenes. They are increasingly used for hand gesture recognition. This paper is also related to this problem. In contrast to other works which try to segment the hands we propose using point cloud processing and the Viewpoint Feature Histogram (VFH) as the global descriptor of the scene. To empower the distinctiveness of the descriptor a modification is proposed which consists in dividing the work space into smaller cells and calculating the VFH for each of them. The method is applied to five sample static gestures which are relatively difficult to recognise because hands are not the objects nearest the camera and/or touch each other, touch the head or appear in the background of the face. Results of ten-fold cross validation that justify the proposed approach are given.

Keywords

hand gesture recognition time-of-flight camera point cloud viewpoint feature histogram 

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References

  1. 1.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Cambridge (2008)Google Scholar
  2. 2.
    Chiabrandoa, F., Piatti, D., Rinaudo, F.: SR-4000 ToF Camera: Further Eexperimental Tests and First Application to Metric Surveys. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII, 149–154 (2010)Google Scholar
  3. 3.
    Haubner, N., Schwanecke, U., Drner, R., Lehmann, S., Luderschmidt, J.: Recognition of Dynamic Hand Gestures with Time-of-Flight Cameras. In: Proceedings of ITG/GI Workshop on Self-Integrating Systems for Better Living Environments (Sensyble Workshop), pp. 33–39 (2010)Google Scholar
  4. 4.
    Mason, M., Duric, Z.: Using Histograms to Detect and Track Objects in Color Video. In: Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop, pp. 154–159 (2001)Google Scholar
  5. 5.
    Molina, J., Escudero-Vinolo, M., Signoriello, A., Pardas, M., Ferran, C., Bescos, J., Marques, F., Martinez, J.M.: Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models. Machine Vision and Applications 24, 187–204 (2013)CrossRefGoogle Scholar
  6. 6.
    Oprisescu, S., Rasche, C., Su, B.: Automatic static hand gesture recognition using ToF cameras. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 2748–2751 (2012)Google Scholar
  7. 7.
    Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram. In: Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2155–2162 (2010)Google Scholar
  8. 8.
    Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Learning informative point classes for the acquisition of object model maps. In: 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 643–650 (2008)Google Scholar
  9. 9.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast Point Feature Histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3212–3217 (2009)Google Scholar
  10. 10.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation, ICRA (2011)Google Scholar
  11. 11.
    Song, L., Hu, R., Xiao, Y., Gong, L.: Real-Time 3D Hand Tracking from Depth Images. In: Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM-13), pp. 1119–1122 (2013)Google Scholar
  12. 12.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2008)Google Scholar
  13. 13.
    Uebersax, D., Gall, J., Van den Bergh, M., Van Gool, L.: Real-time sign language letter and word recognition from depth data. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 383–390 (2011)Google Scholar
  14. 14.
    Van den Bergh, M., Van Gool, L.: Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 66–72 (2011)Google Scholar
  15. 15.
    Zahedi, M., Manashty, A.R.: Robust Sign Language Recognition System Using ToF Depth Cameras. World of Computer Science and Information Technology Journal (WCSIT) 1, 50–55 (2011)Google Scholar
  16. 16.
    Mesa Imaging AG SwissRanger4000, http://www.mesa-imaging.ch/
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Tomasz Kapuściński
    • 1
  • Mariusz Oszust
    • 1
  • Marian Wysocki
    • 1
  1. 1.Department of Computer and Control EngineeringRzeszow University of TechnologyRzeszowPoland

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