Random model variation for universal feature tracking

Abstract

Feature based tracking approaches become more and more common for Augmented Reality (AR). However, most upcoming AR solutions are designed for mobile devices, in particular for smartphones and tablet computers, lacking sufficient performance for the execution of state-of-the art feature based approaches at interactive frame rates. In this paper we will present our approach significantly increasing the speed of feature based tracking, thus allowing for real-time applications even on mobile devices. Our approach applies a randomized pose initialization, is applicable to any feature detector and does not require any feature appearance attributes, such as descriptors or ferns.