μ-UAV Based Dynamic Target Tracking for Surveillance and Exploration

  • Harish Bhaskar
  • Jorge Dias
  • Lakmal Seneviratne
  • Mohammed Al-Mualla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)


This paper presents an autonomous computer vision system for tracking multiple dynamic ground targets, from images acquired by a camera onboard of a μ-UAV. The method proposed is a self adaptive technique that seamlessly integrates ego-motion compensation with target detection and tracking to provide robust localisation of ground targets. Ego-motion compensation is achieved through establishing homographies using target independent invariant feature descriptors. Targets are then detected using a novel background learning strategy where the optical flow field is fused together with a dynamic background model for accurate foreground extraction. In addition, the paper also reports the use of a Monte Carlo joint probabilistic data association filter for tracking multiple unknown targets. The field tests demonstrate the capabilities of the vision system based on experimental results on images captured by a camera on-board of quadrators (μ-UAV).


Gaussian Mixture Model Target Detection Target Tracking Ground Target Gaussian Probability Density Function 
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.
    Cesetti, A., Frontoni, E., Mancini, A., Zingaretti, P., Longhi, S.: Vision-based autonomous navigation and landing of an unmanned aerial vehicle using natural landmarks. In: Proc. 17th Mediterranean Conference on Control and Automation, pp. 910–915 (2009)Google Scholar
  2. 2.
    Teuliére, C., Eck, L., Marchand, E.: Chasing a moving target from a flying uav. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS 2011, San Francisco, USA, pp. 4929–4934 (2011)Google Scholar
  3. 3.
    Wang, Z.R., Jia, Y.L., Huang, H., Tang, S.M.: Pedestrian detection using boosted hog features. In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 1155–1160 (2008)Google Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    Lei, C., Yang, Y.H.: Optical flow estimation on coarse-to-fine region-trees using discrete optimization. In: ICCV, pp. 1562–1569 (2009)Google Scholar
  7. 7.
    Liu, C., Freeman, W., Adelson, E., Weiss, Y.: Human-assisted motion annotation, pp. 1–8 (2008)Google Scholar
  8. 8.
    Hayman, E., Eklundh, J.O.: Statistical background subtraction for a mobile observer. In: Proc. of the Intl. Conf. on Computer Vision, pp. 67–74 (2003)Google Scholar
  9. 9.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. of IEEE Conf. CVPR, pp. 246–252 (1999)Google Scholar
  10. 10.
    Chang, F., Chen, C.-J., Lu, C.-J.: A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding 93, 206–220 (2004)CrossRefGoogle Scholar
  11. 11.
    Jaward, M., Mihaylova, L., Canagarajah, N., D, B.: Multiple objects tracking using particle filters in video sequences. In: Proc. of the IEEE Aerospace Conf., Big Sky, MT, USA (2006)Google Scholar
  12. 12.
    UCF: Aerial action data set. Technical report (2009),
  13. 13.
    Charoenpong, T., Supasuteekul, A., Nuthong, C.: Adaptive background modeling from an image sequence by using k-means clustering. In: 2010 International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 880–883 (2010)Google Scholar
  14. 14.
    Sheikh, Y., Javed, O., Kanade, T.: Background Subtraction for Freely Moving Cameras. In: IEEE 12th International Conference on Computer Vision, pp. 1219–1225. IEEE (2009)Google Scholar
  15. 15.
    Maggio, E., Cavallaro, A.: Hybrid particle filter and mean shift tracker with adaptive transition model. In: Proc. Int. Conf. Acoustics, Speech, and Signal Processing, pp. 221–224 (2005)Google Scholar
  16. 16.
    Souded, M., Giulieri, L., Bremond, F.: An object tracking in particle filtering and data association framework, using sift features. In: International Conference on Imaging for Crime Detection and Prevention (ICDP) (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Harish Bhaskar
    • 1
  • Jorge Dias
    • 1
  • Lakmal Seneviratne
    • 1
  • Mohammed Al-Mualla
    • 1
  1. 1.Technology and ResearchKhalifa University of ScienceAbu DhabiU.A.E.

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