• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.38-43
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• 2021

As the number of augmented reality games using augmented reality technology increases, the demands of users are also increasing. Game technologies used in augmented reality games are mainly games using MARKER, MARKERLESS, GPS, etc. Games using this technology can augment the background and other objects. To solve this problem, we want to help develop augmented reality games by analyzing objects in the background, which is an important element of augmented reality. To analyze the background in the augmented reality game, the background object was analyzed by applying a deep learning model using TensorFlow Lite in the UNITY engine. Using this result, we obtained the result that augmented objects can be placed in the game according to the types of objects analyzed in the background. By utilizing this research, it will be possible to develop advanced augmented reality games by augmenting objects that fit the background.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.341-350
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• 2022

In this paper, we propose a deep learning based on-device augmented reality (AR) system in which multiple input images are used to implement the correct occlusion in a real environment. The proposed system is composed of three technical steps; camera pose estimation, depth estimation, and object augmentation. Each step employs various mobile frameworks to optimize the processing on the on-device environment. Firstly, in the camera pose estimation stage, the massive computation involved in feature extraction is parallelized using OpenCL which is the GPU parallelization framework. Next, in depth estimation, monocular and multiple image-based depth image inference is accelerated using the mobile deep learning framework, i.e. TensorFlow Lite. Finally, object augmentation and occlusion handling are performed on the OpenGL ES mobile graphics framework. The proposed augmented reality system is implemented as an application in the Android environment. We evaluate the performance of the proposed system in terms of augmentation accuracy and the processing time in the mobile as well as PC environments.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.79-89
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• 2022

With the popularity and the advanced graphics hardware technology of mobile devices, various mobile applications that help children with physical activities have been studied. This paper presents SandUp, a mobile application that guides the play of building sand castles using artificial intelligence and augmented reality(AR) technology. In the process of building the sandcastle, children can interactively explore the target virtual sandcastle through the smartphone display using AR technology. In addition, to help children complete the sandcastle, SandUp informs the sand shape and task required step by step and provides visual and auditory feedback while recognizing progress in real-time using the phone's camera and deep learning classification. We prototyped our SandUp app using Flutter and TensorFlow Lite. To evaluate the usability and effectiveness of the proposed SandUp, we conducted a questionnaire survey on 50 adults and a user study on 20 children aged 4~7 years. The survey results showed that SandUp effectively helps build the sandcastle with proper interactive guidance. Based on the results from the user study on children and feedback from their parents, we also derived usability issues that can be further improved and suggested future research directions.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.635-640
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• 2021

In this paper, we describe a parking space identification system that can automatically identify empty parking lot spaces from a parking lot photo. This system is based on a deep learning technique, and the accuracy of the identification result is good by learning various existing parking lot images. It could be applied to the existing parking management system. This system was also developed as a smartphone application for easy testing. Therefore, if you take a picture of a parking lot through a smartphone camera, the captured image is automatically recognized and an empty parking space can be automatically identified.