scalable recognition and tracking for mobile augmented reality
For an augmented reality application to be realistic, exact tracking of target objects is essential. However, recent mobile augmented reality applications such as location-based applications or recognition-based applications, showed less quality of realistic augmentation due to inexact tracking methods. Vision based tracking is capable of being exact and robust, but as a mobile augmented reality system, the number of objects it can augment was far limited. In this paper, we propose a new framework that overcomes limitations of previous works in two points. One, our framework is scalable to the number of objects being augmented. Two, our framework provides improved realistic augmentation adopting real-time accurate visual tracking method. To our best knowledge, there has been no system proposed successfully integrating both properties. To achieve scalability, bag of visual words based recognition module with large database runs on remote server and mobile phone tracks and augments the target object by itself. The server and mobile phone is connected through conventional Wi-Fi. Including network latency, our implementation takes 0.2sec for initiating AR service on 10,000 object database, which is acceptable in real-world augmented reality application.
scalable recognition mobile augmented reality mobile phones visual detection and tracking