Adaptive Lucas-Kanade tracking

Dense image alignment, when the displacement between the frames is large, can be a challenging task. This paper presents a novel dense image alignment algorithm, the Adaptive Forwards Additive Lucas-Kanade (AFA-LK) tracking algorithm, which considers the scale-space representation of the images, parametrized by a scale parameter, to estimate the geometric transformation between an input image and the corresponding template. The main result in this framework is the optimization of the scale parameter along with the transformation parameters, which permits to significantly increase the convergence domain of the proposed algorithm while keeping a high estimation precision. The performance of the proposed method was tested in various computer-based experiments, which reveal its interest in comparison with geometric as well as learning-based methods from the literature, both in terms of precision and convergence rate. (C) 2019 Elsevier B.V. All rights reserved.