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Recent mobile device and vision technology advances have enabled mobile Augmented Reality (AR) to be serviced in realtime using natural features. However, in viewing augmented reality while moving about, the user is always encountering new and diverse target objects in different locations. Whether the AR system is scalable or not to the number of target objects is an important issue for future mobile AR services. But this scalability has been far limited due to the small capacity of internal storage and memory of the mobile devices. In this paper, a new framework is proposed that achieves scalability for mobile augmented reality. The scalability is achieved by using a bag of visual words based recognition module on the server side with connected through conventional Wi-Fi. On the client side, the mobile phone tracks and augments based on natural features in real-time. In the experiment, it takes 0.2 seconds for the cold start of an AR service initiated on a 10k object database with recognition accuracy 95%, which is acceptable for a real-world mobile AR application. INTRODUCTIONAs smart phones get more popular these days, mobile Augmented Reality (AR) is also getting more attention. Early mobile AR applications can be categorized into two: locationbased AR applications and recognition-based AR applications. In location-based AR, the GPS and the compass sensor are used. As these sensors have a large error on tracking the user's point of view, the augmented result is decoupled from the real world visual stream. The recognition-based AR focuses on providing additional information from the recognition result. It is also decoupled from the real world visual stream. For a more immersive and realistic AR experience, the augmented result requires the use of the real world visual stream, which we refer to as visual-tracking based AR.In the past, vision-based AR applications were not available on mobile devices due to their computational limitation, but recently the limitation has been reduced significantly. Embedded processors in the mobile phones have become much more powerful than previously. By redesigning or tuning PC-based vision algorithms for the mobile device environment, state-of-theart works such as SIFT and Ferns have been successively ported to the mobile platforms [10]. The detection and tracking methods suited for mobile phones have been proposed and verified [11].There is some works exist addressing scalability issues. In [9], they showed a similar framework which combines a scalable recognition module and a detection/tracking module. However it was not a mobile framework and the scalability is only showed for hundreds of objects. In [6], they fully outsourced the detection/tracking through a distributed network, but they didn't show its scalability.In this paper, we propose a new mobile AR framework that is scalable to the number of objects being augmented and provides an elaborated level of tracking. Scalability is achieved with a scalable recognition module on the server side. The elaborated level of tra...

An augmented reality book (AR book) is an application in which such multimedia elements as virtual 3D objects, movie clips, or sound clips are augmented to a conventional book using augmented reality technology. It can provide better understanding about the contents and visual impressions for users. For AR books, this paper presents a markerless tracking method, which recognizes and tracks a large number of pages in real-time, even on PCs with low computation power. For fast recognition with respect to a large number of pages, we propose a generic randomized forest that is an extension of a randomized forest. In addition, we define the spatial locality of the subregions in an image to resolve the problem of a dropping recognition rate under a complex background. For tracking with minimal jittering, we also propose the adaptive keyframe-based tracking method, which automatically updates the current frame as a keyframe when it describes the page better than the existing one.

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