Jaewon Ha

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.

Class-based storage assignment policy in carousel system

Abstract The carousel storage system is an automated warehousing system which has been widely used in a broad range of applications. This paper examines the effects of storage assignment policy on the throughput performance of the carousel system in which a storage/retrieval machine performs pickup/discharge operations. For 2-class-based storage assignment policy, the expected cycle time models are developed for single and dual command cycles. It is shown that with 2-class-based storage assignment policy significant reductions in cycle time are obtainable over randomized storage assignment policy. The effects of the system parameters such as the skewness of the inventory distribution, the shape factor and the pickup/discharge time are studied on the optimal boundary.

Cycle time models for single/double carousel system

Abstract The carousel storage system is an automated storage/retrieval system; it has been widely used in a broad range of applications. This paper examines the throughput performance of the single/double carousel system in which a storage/retrieval machine performs pickup/discharge operations. As a job dispatch and control rule, it is assumed that the information on the rack location of the succeeding job orders is available, this assumption commonly holds in real-world operations. Based on a randomized storage assignment policy, cycle time models are developed for single and dual commands. The value of the information on the succeeding jobs is evaluated in terms of the system efficiency. Also, the effects of the rack shape factor and pickup/discharge time of the storage/retrieval robot are analyzed with the help of sample problems.

Novel Applications of VR: Efficient mobile AR technology using scalable recognition and tracking based on server-client model

Advancements in mobile devices and vision technology have enabled mobile Augmented Reality (AR) to be serviced in real-time using natural features. However, in viewing AR while moving around in the real world, users often encounter new and diverse target objects. Whether the AR system is scalable to the number of target objects is a very crucial issue for mobile AR services in the real world. This scalability, however, has been severely limited because of the small internal storage capacity and memory of the mobile devices. In this paper, a new framework is proposed that achieves scalability for mobile AR. The scalability is achieved with a bag-of-visual-words based recognition module on the server side that is connected to the clients, which are mobile devices, through a conventional Wi-Fi network. On the client side, the coarse-to-fine tracking module enables robust tracking performance with natural features in real-time. In this study, we optimized modules in mobile devices for expediting pose-tracking processing and simultaneously enabled 3D rendering and animation in real-time. We also propose an efficient recognition method in which metadata are provided by the sensors of mobile devices. In the experiment, it takes approximately 0.2s for the cold start of an AR service initiated on a 10K object database with a recognition accuracy of 99.87%, which should be acceptable for a variety of real-world mobile AR applications.

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.

An optimal boundary for two-class based storage assignment policy in carousel system

Abstract This paper examines the effects of 2-class-based turnover assignment policy on the throughput performance of the carousel system in which a storage/retrieval machine performs pickup/discharge operations. Two alternative configurations in class boundary shape are considered. For each alternative, the expected cycle time is developed for single and dual command cycles based on the continuous analytical models. It is shown that with 2-class-based turnover assignment policy(C2) significant reductions in cycle time are obtainable over randomized storage assignment policy. The effects of the system parameters such as the skewness of the inventory distribution, the shape factor and the pickup/discharge time are studied on the optimal boundary.

Mobile Augmented Reality using scalable recognition and tracking

In this paper, a new mobile Augmented Reality (AR) framework which is scalable to the number of objects being augmented is proposed. The scalability is achieved by a visual word recognition module on the remote server and a mobile phone which detects, tracks, and augments target objects with the received information from the server. The server and the mobile phone are connected through a conventional Wi-Fi. In the experiment, it takes 0.2 seconds for the cold start of an AR service initiation on a 10k object database, which is fairly acceptable in a real-world AR application.

Development of an efficient face detection and tracking system for mobile devices

In this paper, we propose efficient face detection and tracking system for mobile interaction. To detect face rapidly, the proposed system adopts color filtering based efficient region selection method. Then the proposed system tracks the detected face based on particle filter. By integrating efficient face detector and face tracker, the proposed system can be used as user interface for mobile interaction. A set of experiment on PC and mobile environment are presented. The proposed system greatly reduce overall computational time and number of false positives.