Real-time scalable recognition and tracking based on the server-client model for mobile Augmented Reality

Recent mobile device and vision technology advances have enabled mobile Augmented Reality (AR) to be serviced in real-time 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.