We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target. A Shannon entropy measure is used to pick the most informative locations of these features. This minimizes the number of Ferns while maximizing its discriminative power, allowing thus, for robust detections at low computational costs. In addition, after offline initialization, the new incoming detections are used to update the posterior probabilities on the fly, and adapt to changing appearances that can occur due to the presence of shadows or occluding objects. All these virtues, make the proposed detector appropriate for UAV navigation. Besides the synthetic experiments that will demonstrate the theoretical benefits of our formulation, we will show applications for detecting landing areas in regions with highly repetitive patterns, and specific objects under the presence of cast shadows or sudden camera motions.


aerospace robotics, computer vision, pattern recognition.

Author keywords

object detection,online learning, autonomous robots

Scientific reference

M. Villamizar, A. Sanfeliu and F. Moreno-Noguer. Fast online learning and detection of natural landmarks for autonomous aerial robots, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, China, pp. 4996-5003.