High-Speed Hand Tracking for Studying Human-Computer Interaction

  • Toni Kuronen
  • Tuomas Eerola
  • Lasse Lensu
  • Jari Takatalo
  • Jukka Häkkinen
  • Heikki Kälviäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)


Understanding how a human behaves while performing human-computer interaction tasks is essential in order to develop better user interfaces. In the case of touch and gesture based interfaces, the main interest is in the characterization of hand movements. The recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. However, the tracking algorithm development has been focused on optimizing the robustness and computation speed instead of spatial accuracy, making most of them, as such, insufficient for the accurate measurements of hand movements. In this paper, state-of-the-art tracking algorithms are compared based on their suitability for the finger tracking during human-computer interaction task. Furthermore, various trajectory filtering techniques are evaluated to improve the accuracy and to obtain appropriate hand movement measurements. The experimental results showed that Kernelized Correlation Filters and Spatio-Temporal Context Learning tracking were the best tracking methods obtaining reasonable accuracy and high processing speed while Local Regression filtering and Unscented Kalman Smoother were the most suitable filtering techniques.


Hand tracking High-speed video Hand trajectories Filtering Human-computer interaction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  2. 2.
    Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision 20(1–2), 89–97 (2004)MathSciNetGoogle Scholar
  3. 3.
    Cleveland, W.S.: Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association 74(368), 829–836 (1979)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: Vision-based hand pose estimation: A review. Computer Vision and Image Understanding 108(1–2), 52–73 (2007). special Issue on Vision for Human-Computer InteractionCrossRefGoogle Scholar
  5. 5.
    Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. Computer Vision and Image Understanding 117(10), 1245–1256 (2012)CrossRefGoogle Scholar
  6. 6.
    Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: IEEE International Conference on Computer Vision (ICCV), pp. 263–270 (2011)Google Scholar
  7. 7.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3), 583–596 (2015)CrossRefGoogle Scholar
  9. 9.
    Hiltunen, V., Eerola, T., Lensu, L., Kälviäinen, H.: Comparison of general object trackers for hand tracking in high-speed videos. In: International Conference on Pattern Recognition (ICPR), pp. 2215–2220 (2014)Google Scholar
  10. 10.
    Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(12), 2420–2440 (2012)CrossRefGoogle Scholar
  11. 11.
    Julier, S.J., Uhlmann, J.K.: A new extension of the kalman filter to nonlinear systems. In: Proceedings of The International Society for Optics and Photonics (SPIE) AeroSense: International Symposium on Aerospace/Defense Sensing, Simulations and Controls (1997)Google Scholar
  12. 12.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)CrossRefGoogle Scholar
  13. 13.
    Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 191–217. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  14. 14.
    Kuronen, T.: Post-Processing and Analysis of Tracked Hand Trajectories. Master’s thesis, Lappeenranta University of Technology (2014)Google Scholar
  15. 15.
    Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Hengel, A.V.D.: A Survey of Appearance Models in Visual Object Tracking. ACM Transactions on Intelligent Systems and Technology (TIST) 4(4), 58:1–58:48 (2013)Google Scholar
  16. 16.
    Luo, W.: Matlab code for Multiple Instance Learning (MIL) Tracker. (accessed: August, 2013)
  17. 17.
    Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 1985–1991 (2003)Google Scholar
  18. 18.
    Orfanidis, S.J.: Introduction to Signal Processing. Prentice Hall international editions, Prentice Hall (1996–2009)Google Scholar
  19. 19.
    Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision 77(1–3), 125–141 (2008)CrossRefGoogle Scholar
  20. 20.
    Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Publishing (1997)Google Scholar
  21. 21.
    Wang, D., Lu, H., Yang, M.H.: Online Object Tracking With Sparse Prototypes. IEEE Transactions on Image Processing 22(1), 314–325 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Tech. rep. Department of Computer Science, University of North Carolina (1995)Google Scholar
  23. 23.
    Welch, G., Bishop, G.: An Introduction to the kalman filter: SIGGRAPH 2001 course 8. In: Computer Graphics, Annual Conference on Computer Graphics & Interactive Techniques, pp. 12–17 (2001)Google Scholar
  24. 24.
    Wu, Y., Lim, J., Yang, M.H.: Object Tracking Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)Google Scholar
  25. 25.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computing Surveys 38(4) (2006)Google Scholar
  26. 26.
    Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 127–141. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  27. 27.
    Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  28. 28.
    Zhang, K., Zhang, L., Yang, M.H.: Fast Compressive Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(10), 2002–2015 (2014)CrossRefGoogle Scholar
  29. 29.
    Zhong, W., Lu, H., Yang, M.H.: Robust Object Tracking via Sparse Collaborative Appearance Model. IEEE Transactions on Image Processing 23(5), 2356–2368 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Toni Kuronen
    • 1
  • Tuomas Eerola
    • 1
  • Lasse Lensu
    • 1
  • Jari Takatalo
    • 2
  • Jukka Häkkinen
    • 2
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Laboratory (MVPR), School of Engineering ScienceLappeenranta University of Technology (LUT)LappeenrantaFinland
  2. 2.Visual Cognition Research Group, Institute of Behavioural SciencesUniversity of HelsinkiHelsinkiFinland

Personalised recommendations