Robust Visual Detection–Learning–Tracking Framework for Autonomous Aerial Refueling of UAVs

Abstract

In this paper, we propose a robust visual detection-learning-tracking framework for autonomous aerial refueling of unmanned aerial vehicles. Two classifiers (D-classifier and T-classifier) are defined in the proposed framework. The D-classifier is a robust linear support vector machine (SVM) classifier trained offline for detecting the drogue object of aerial refueling and a low-dimensional normalized robust local binary pattern feature is proposed to describe the drogue object in the D-classifier. The T-classifier is a state-based structured SVM classifier trained online for tracking the drogue object. A combination strategy between the D-classifier and the T-classifier is proposed in the framework. The D-classifier is used to assess if some positive support vectors in the T-classifier are required to be replaced by positive examples with density peaks. The experimental results on several challenging video sequences validate the effectiveness and robustness of our proposed framework.