Landmarks recognition for autonomous aerial navigation by neural networks and Gabor transform

Abstract

Template matching in real-time is a fundamental issue in many applications in computer vision such as tracking, stereo vision and autonomous navigation. The goal of this paper is present a system for automatic landmarks recognition in video frames over a georeferenced high resolution satellite image, for autonomous aerial navigation research. The video frames employed were obtained from a camera fixed to a helicopter in a low level flight, simulating the vision system of an unmanned aerial vehicle (UAV). The landmarks descriptors used in recognition task were texture features extracted by a Gabor Wavelet filters bank. The recognition system consists on a supervised neural network trained to recognize the satellite image landmarks texture features. In activation phase, each video frame has its texture feature extracted and the neural network has to classify it as a predefined landmark. The video frames are also preprocessed to reduce their difference of scale and rotation from the satellite image before the texture feature extraction, so the UAV altitude and heading for each frame are considered as known. The neural network techniques present the advantage of low computational cost, been appropriate to real-time applications. Promising results were obtained, mainly during flight over urban areas.