Robust Perception for Aerial Inspection: Adaptive and On-Line Techniques

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 129)


This chapter explains an adaptive on-line object detection and classification technique for robust perception due to varying scene conditions, for example partial cast shadows, change on the illumination conditions or changes in the angle of the object target view. This approach continuously updates the target model upon arrival of new data, being able to adapt to dynamic situations. The method uses an on-line learning technique that works on real-time and it is continuously updated in order to adapt to potential changes undergone by the target object. The method can run in real-time.


  1. 1.
    Kim, J., Shim, D.H.: A vision-based target tracking control system of a quadrotor by using a tablet computer. 1165–1172 (2013)Google Scholar
  2. 2.
    Masselli, A., Yang, S., Wenzel, K.E., Zell, A.: A cross-platform comparison of visual marker based approaches for autonomous flight of quadrocopters. 685–693 (2013)Google Scholar
  3. 3.
    Mondragon, I.F., Campoy, P., Correa, J.F., Mejias, L.: Visual model feature tracking for uav control. 1–6 (2007)Google Scholar
  4. 4.
    Flores, G., Zhou, S., Lozano, R., Castillo, P.: A vision and gps-based real-time trajectory planning for mav in unknown urban environments. 1150–1155 (2013)Google Scholar
  5. 5.
    Yang, S., Scherer, S.A., Zell, A.: An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. 69(1–4), 499–515 (2013)Google Scholar
  6. 6.
    Fan, Y., Haiqing, S., Hong, W.: A vision-based algorithm for landing unmanned aerial vehicles. 993–996 (2008)Google Scholar
  7. 7.
    Sanchez-Lopez, J.L., Saripalli, S., Campoy, P., Pestana, J., Fu, C.: Toward visual autonomous ship board landing of a vtol uav. 779–788 (2013)Google Scholar
  8. 8.
    Villamizar, M., Sanfeliu, A., Moreno-Noguer, F.: Fast online learning and detection of natural landmarks for autonomous aerial robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4996–5003 (2014)Google Scholar
  9. 9.
    Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1465–1479 (2006)CrossRefGoogle Scholar
  10. 10.
    Moreno-Noguer, F., Lepetit, V., Fua, P.: Pose priors for simultaneously solving alignment and correspondence. In: Proceedings of the IEEE European Conference on Computer Vision (ECCV), vol. 2, pp. 405–418 (2008)Google Scholar
  11. 11.
    Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 448–461 (2010)CrossRefGoogle Scholar
  12. 12.
    Villamizar, M., Andrade-Cetto, J., Sanfeliu, A., Moreno-Noguer, F.: Boosted random ferns for object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2017)Google Scholar
  13. 13.
    Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. FTCGV 7(2–3), 81–227 (2011)zbMATHGoogle Scholar
  14. 14.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49–56 (2010)Google Scholar
  15. 15.
    Villamizar, M., Garrell, A., Sanfeliu, A., Moreno-Noguer, F.: Online human-assisted learning using random ferns. In: Proceedings International Conference on Pattern Recognition (ICPR), pp. 2821–2824 (2012)Google Scholar
  16. 16.
    Breiman, L.: Random forests. ML 45(1), 5–32 (2001)zbMATHGoogle Scholar
  17. 17.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)Google Scholar
  18. 18.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.CSIC-UPC, Institut de Robòtica i Informàtica IndustrialBarcelonaSpain

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