Computationally efficient retrieval-based tracking system and augmented reality for large-scale areas

We present a retrieval-based tracking system that requires less computational time and cost. The system tracks a user's location through a small portion of an image captured by the camera, and then refines the camera pose by propagating matchings to the whole image. Augmented information such as building names and locations will be delivered to the user. The progressive way to process image data not only can provide the user with location information at real-time speed, but more importantly, it reduces the feature matching time by limiting the searching ranges. The proposed system contains two parts, offline database building and online user tracking. The database is composed of image patches with features and location information. The images are captured at different locations of interests from different viewing angles and distances, and then these images are partitioned into smaller patches. The location of a user can be calculated by querying one or more patches of the captured image. Moreover, the system is capable to handle large occlusions in images due to the patch approach. Experiments show that the proposed tracking system is efficient and robust in many different environments.