Real-time camera orientation estimation based on vanishing point tracking under Manhattan World assumption

This paper proposes a real-time pipeline for estimating the camera orientation based on vanishing points for indoor navigation assistance on a Smartphone. The orientation of embedded camera relies on the ability to find a reliable triplet of orthogonal vanishing points. The proposed pipeline introduces a novel sampling strategy among finite and infinite vanishing points with a random sample consensus-based line clustering and a tracking along a video sequence to enforce the accuracy and the robustness by extracting the three most pertinent orthogonal directions while preserving a short processing time for real-time application. Experiments on real images and video sequences acquired with a Smartphone show that the proposed strategy for selecting orthogonal vanishing points is pertinent as our algorithm gives better results than the recently published RNS optimal method, in particular for the yaw angle, which is actually essential for the navigation task.