Visual Model Feature Tracking For UAV Control

Abstract

This paper explores the possibilities to use robust object tracking algorithms based on visual model features as generator of visual references for UAV control. A scale invariant feature transform (SIFT) algorithm is used for detecting the salient points at every processed image, then a projective transformation for evaluating the visual references is obtained using a version of the RANSAC algorithm, in which a series of matched key-points pairs that fulfill the transformation equations are selected, rejecting otherwise the corrupted data. The system has been tested using diverse image sequences showing its capability to track objects significantly changed in scale, position, rotation, generating at the same time velocity references to the UAV flight controller. The robustness our approach has also been validated using images taken from real flights showing noise and lighting distortions. The results presented are promising in order to be used as reference generator for the control system.