Abstract
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.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kim, J., Shim, D.H.: A vision-based target tracking control system of a quadrotor by using a tablet computer. 1165–1172 (2013)
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)
Mondragon, I.F., Campoy, P., Correa, J.F., Mejias, L.: Visual model feature tracking for uav control. 1–6 (2007)
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)
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)
Fan, Y., Haiqing, S., Hong, W.: A vision-based algorithm for landing unmanned aerial vehicles. 993–996 (2008)
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)
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)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1465–1479 (2006)
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)
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)
Villamizar, M., Andrade-Cetto, J., Sanfeliu, A., Moreno-Noguer, F.: Boosted random ferns for object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2017)
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)
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)
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)
Breiman, L.: Random forests. ML 45(1), 5–32 (2001)
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)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Villamizar, M., Sanfeliu, A. (2019). Robust Perception for Aerial Inspection: Adaptive and On-Line Techniques. In: Ollero, A., Siciliano, B. (eds) Aerial Robotic Manipulation. Springer Tracts in Advanced Robotics, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-030-12945-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-12945-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12944-6
Online ISBN: 978-3-030-12945-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)