Vision-based tracking in large image database for real-time mobile augmented reality

This paper presents an approach for tracking natural objects in augmented reality applications. The targets are detected and identified using a markerless approach relying upon the extraction of image salient features and descriptors. The method deals with large image databases using a novel strategy for feature retrieval and pairwise matching. Further-more, the developed method integrates a real-time solution for 3D pose estimation using an analytical technique based on camera perspective transformations. The algorithm associates 2D feature samples coming from the identification part with 3D mapped points of the object space. Next, a sampling scheme for ordering correspondences is carried out to establishing the 2D/3D projective relationship. The tracker performs localization using the feature images and 3D models to enhance the scene view with overlaid graphics by computing the camera motion parameters. The modules built within this architecture are deployed on a mobile platform to provide an intuitive interface for interacting with the surrounding real world. The system is experimented and evaluated on challenging scalable image dataset and the obtained results demonstrate the effectiveness of the approach towards versatile augmented reality applications.