Telemetry-Based Search Window Correction for Airborne Tracking

  • Pau Climent-Pérez
  • Georgios Lazaridis
  • Georg Hummel
  • Martin Russ
  • Dorothy N. Monekosso
  • Paolo Remagnino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)


Tracking from airborne cameras is very challenging, since most assumptions made for fixed cameras do not hold. Therefore, compensation of platform ego-motion is seen as a necessary pre-processing step. Most existing methods perform image registration or matching, which involves costly image transformations, and have a restricted operational range. In this paper, a novel ego-motion compensation approach is presented, that transforms the local search window of the visual tracker. This is much more computationally efficient, and can be applied regardless of the amount of texture in the background. Experiments with ground truth and tracker output data are conducted and show the validity of the approach.


Unmanned Aerial Vehicle Inertial Measurement Unit Visual Tracker Search Window Ground Truth Data 
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.
    Turner, D., Lucieer, A., Watson, C.: An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (uav) imagery, based on structure from motion (SfM) point clouds. Remote Sensing 4, 1392–1410 (2012)CrossRefGoogle Scholar
  2. 2.
    Shastry, A., Schowengerdt, R.: Airborne video registration and traffic-flow parameter estimation. IEEE Transactions on Intelligent Transportation Systems 6, 391–405 (2005)CrossRefGoogle Scholar
  3. 3.
    Andriluka, M., Schnitzspan, P., Meyer, J., Kohlbrecher, S., Petersen, K., Von Stryk, O., Roth, S., Schiele, B.: Vision based victim detection from unmanned aerial vehicles. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1740–1747 (2010)Google Scholar
  4. 4.
    Sharp, C., Shakernia, O., Sastry, S.: A vision system for landing an unmanned aerial vehicle. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001, vol. 2, pp. 1720–1727 (2001)Google Scholar
  5. 5.
    Reinartz, P., Lachaise, M., Schmeer, E., Krauss, T., Runge, H.: Traffic monitoring with serial images from airborne cameras. ISPRS Journal of Photogrammetry and Remote Sensing 61, 149–158 (2006)CrossRefGoogle Scholar
  6. 6.
    Hummel, G., Kovács, L., Stütz, P., Szirányi, T.: Data Simulation and Testing of Visual Algorithms in Synthetic Environments for Security Sensor Networks. In: Aschenbruck, N., Martini, P., Meier, M., Tölle, J. (eds.) Future Security. CCIS, vol. 318, pp. 212–215. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Remagnino, P., Brand, P., Mohr, R.: Correlation techniques in adaptive template matching with uncalibrated cameras, vol. 2356, pp. 252–263 (1995)Google Scholar
  8. 8.
    Yuan, C., Recktenwald, F., Mallot, H.: Visual steering of uav in unknown environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 3906–3911 (2009)Google Scholar
  9. 9.
    Plinval, H., Morin, P., Mouyon, P., Hamel, T.: Visual servoing for underactuated vtol uavs: a linear, homography-based framework. In: International Journal of Robust and Nonlinear Control, pp. 1–24 (2013)Google Scholar
  10. 10.
    Nemra, A., Aouf, N.: Robust feature extraction and correspondence for uav map building. In: 17th Mediterranean Conference on Control and Automation, MED 2009, pp. 922–927 (2009)Google Scholar
  11. 11.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Unmanned Aerial Vehicle Localization Based on Monocular Vision and Online Mosaicking. Journal of Intelligent and Robotic Systems 55, 323–343 (2009)CrossRefzbMATHGoogle Scholar
  12. 12.
    Heredia, G., Caballero, F., Maza, I., Merino, L., Viguria, A., Ollero, A.: Multi-unmanned aerial vehicle (uav) cooperative fault detection employing differential global positioning (dgps), inertial and vision sensors. Sensors 9, 7566–7579 (2009)CrossRefGoogle Scholar
  13. 13.
    Zhang, S.: Object tracking in unmanned aerial vehicle (uav) videos using a combined approach. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2005, vol. 2, pp. 681–684 (2005)Google Scholar
  14. 14.
    Barber, D., Redding, J., McLain, T., Beard, R., Taylor, C.: Vision-based target geo-location using a fixed-wing miniature air vehicle. Journal of Intelligent and Robotic Systems 47, 361–382 (2006)CrossRefGoogle Scholar
  15. 15.
    Dobrokhodov, V., Kaminer, I., Jones, K., Ghabcheloo, R.: Vision-based tracking and motion estimation for moving targets using small uavs. In: American Control Conference, pp. 1428–1433 (2006)Google Scholar
  16. 16.
    Teutsch, M., Kruger, W.: Detection, Segmentation, and Tracking of Moving Objects in UAV Videos. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pp. 313–318 (2012)Google Scholar
  17. 17.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Improving vision-based planar motion estimation for unmanned aerial vehicles through online mosaicing. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 2860–2865 (2006)Google Scholar
  18. 18.
    Russ, M., Schmitt, M., Hellert, C., Stütz, P.: Airborne sensor and perception management: Experiments and Results for surveillance UAS. In: AIAA Infotech@Aerospace (I@A) Conference, pp. 1–16 (2013)Google Scholar
  19. 19.
    Böhm, F., Schulte, A.: UAV Autonomy Research - Challenges and advantages of a fully distributed system architecture. In: International Telemetering Conference, ITC 2012, pp. 1–10 (2012)Google Scholar
  20. 20.
    Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on lie algebra. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 728–735. IEEE (2006)Google Scholar
  22. 22.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88, 303–338 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pau Climent-Pérez
    • 1
  • Georgios Lazaridis
    • 1
  • Georg Hummel
    • 2
  • Martin Russ
    • 2
  • Dorothy N. Monekosso
    • 1
  • Paolo Remagnino
    • 1
  1. 1.Robot Vision Team (RoViT), Faculty of Science, Engineering and ComputingKingston University LondonKingston upon ThamesUK
  2. 2.Institute of Flight SystemsUniversity of the Bundeswehr MunichNeubibergGermany

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