A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems

  • Martin DanelljanEmail author
  • Fahad Shahbaz Khan
  • Michael Felsberg
  • Karl Granström
  • Fredrik Heintz
  • Piotr Rudol
  • Mariusz Wzorek
  • Jonas Kvarnström
  • Patrick Doherty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)


Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios.


Visual tracking Visual surveillance Micro UAV Active vision 


  1. 1.
    Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: CVPR (2012)Google Scholar
  2. 2.
    Bar-Shalom, Y., Willett, P.K., Tian, X.: Tracking and data fusion, a handbook of algorithms. YBS (2011)Google Scholar
  3. 3.
    Beard, M., Vo, B., Vo, B.N., Arulampalam, S.: A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering. IEEE Transactions on Aerospace and Electronic Systems 49(4), 2835–2844 (2013)CrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  5. 5.
    Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC (2014)Google Scholar
  6. 6.
    Danelljan, M., Shahbaz Khan, F., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: CVPR (2014)Google Scholar
  7. 7.
    Felsberg, M.: Enhanced distribution field tracking using channel representations. In: ICCV Workshop (2013)Google Scholar
  8. 8.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  9. 9.
    Hare, S., Saffari, A., Torr, P.: Struck: structured output tracking with kernels. In: ICCV (2011)Google Scholar
  10. 10.
    He, S., Yang, Q., Lau, R., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: CVPR (2013)Google Scholar
  11. 11.
    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
  12. 12.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: bootstrapping binary classifiers by structural constraints. In: CVPR (2010)Google Scholar
  13. 13.
    Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A., Lopez, A., Felsberg, M.: Coloring action recognition in still images. IJCV 105(3), 205–221 (2013)CrossRefGoogle Scholar
  14. 14.
    Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A., Vanrell, M., Lopez, A.: Color attributes for object detection. In: CVPR (2012)Google Scholar
  15. 15.
    Kristan, M., et al.: The visual object tracking vot2014 challenge results. In: Bronstein, M., Agapito, L., Rother, C. (eds.) ECCV 2014 Workshops, Part II. LNCS, vol. 8926, pp. xx–yy. Springer, Heidelberg (2015)Google Scholar
  16. 16.
    Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)Google Scholar
  17. 17.
    Liu, B., Huang, J., Yang, L., Kulikowski, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR (2011)Google Scholar
  18. 18.
    Lowe, D.G.: Distinctive image features from scale-invariant points. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  19. 19.
    Mahler, R.: Multitarget Bayes filtering via first-order multi target moments. IEEE Transactions on Aerospace and Electronic Systems 39(4), 1152–1178 (2003)CrossRefGoogle Scholar
  20. 20.
    Mahler, R.: Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood (2007)zbMATHGoogle Scholar
  21. 21.
    Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Pixhawk: A system for autonomous flight using onboard computer vision. In: ICRA (2011)Google Scholar
  22. 22.
    van de Sande, K., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: ICCV (2011)Google Scholar
  23. 23.
    Sinopoli, B., Micheli, M., Donato, G., Koo, T.J.: Vision based navigation for an unmanned aerial vehicle. In: ICRA (2001)Google Scholar
  24. 24.
    Vo, B.N., Ma, W.K.: The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing 54(11), 4091–4104 (2006)CrossRefGoogle Scholar
  25. 25.
    van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning color names for real-world applications. TIP 18(7), 1512–1524 (2009)MathSciNetGoogle Scholar
  26. 26.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR (2013)Google Scholar
  27. 27.
    Yu, Z., Nonami, K., Shin, J., Celestino, D.: 3d vision based landing control of a small scale autonomous helicopter. International Journal of Advanced Robotic Systems 4(1), 51–56 (2007)Google Scholar
  28. 28.
    Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured hog-lbp for object localization. In: CVPR (2010)Google Scholar
  29. 29.
    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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Danelljan
    • 1
    Email author
  • Fahad Shahbaz Khan
    • 1
  • Michael Felsberg
    • 1
  • Karl Granström
    • 1
  • Fredrik Heintz
    • 1
  • Piotr Rudol
    • 1
  • Mariusz Wzorek
    • 1
  • Jonas Kvarnström
    • 1
  • Patrick Doherty
    • 1
  1. 1.Linköping UniversityLinköpingSweden

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