• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.296-297
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• 2019

최근의 IoT 기술의 발전은 다양한 형상, 네트워크 특징 및 서비스 아키텍쳐를 가지는 IoT 기기, 서비스 및 단말을 활용한 서비스의 발전을 가져오고 있다. 특히 OneM2M, OCF 등의 표준기구등은 다양한 IoT 기기 및 서비스 아키텍쳐에 대한 정의를 최근 수년간 진행해 오고 있으며, 이러한 IoT 서비스는 단순히 기기의 원격 상태 확인 및 제어 뿐만 아니라, 클라우드 및 AI 기술과의 연계를 통하여 그 서비스 영역을 지속적으로 확장 중에 있다. 이 중 Internet of Media Things 표준은 다양한 미디어 기반 서비스를 Thing으로 표현하여 다양한 Thing과의 연계 방안을 제시하고 있다. 본 논문에서는 기존에 다양한 기법을 통하여 연구 및 구현되고 있는 영상 기반 서비스를 Internet of Media Things 표준 기반으로 구현하기 위한 방법을 제시한다. 기존 영상 분석 기술은 대부분 정확도의 향상에 그 목적을 가지고 있어 서비스 형태로 제공하고 타 기기와의 연계성을 제공하기 위한 추가적인 기술간 연계가 필요하다. 본 논문에서는 Yolo v3 기반의 Face Detection 기술에 대하여, 해당 기술을 Internet of Media Things 표준으로 표출하기 위한 요구사항을 파악하고 실제 구현하기 위한 방안에 대하여 검토한다.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.459-464
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• 2020

This study is a study that analyzes the user experience of a virtual makeup application in the beauty industry where color or formulation testing is important. Recently, cases of beauty smart stores and beauty applications using AR and AI are increasing. However, since virtual makeup is different from testing a real product, it is necessary to derive needs through research from the user's side. In order to compare user preferences by using AR and AI cases, six factors based on the emotional interface model were analyzed through a questionnaire to identify items with statistically significant figures. As a result, the user felt comfortable with the virtual makeup function, but showed that it needs to be supplemented in terms of reliability. Since this study focused on the customer experience as a real user and identified the main experience factors and needs of virtual makeup through two types of comparison, it is hoped that this study will be useful as a prior study.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1249-1254
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• 2021

Computing technology and networking technology in the era of the 4th industrial revolution are rapidly evolving into an intelligent information society. AR, VR, and MR technologies, which are dual immersive media fields, are being applied in many convergence technologies, especially! The development of the health and healthcare field is actively progressing. In the field of health and healthcare, there are many problems due to aging of the population, increase in chronic progress, lack of infrastructure, and lack of professional manpower. services in the field are adopted. Therefore, this study applies cognitive evaluation through a computing system to the mild cognitive impairment, designs and develops a cognitive judgment platform using augmented reality based on the cognitive judgment technology system design, and integrates AI and BigData-based intelligent cognitive rehabilitation in the future. It is used as basic data for service platform development.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.49-54
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• 2021

The purpose of this study is to identify trends in T commerce and further propose ways to improve customer data-based services and development models for changes in broadcasting screens with the expansion of IPTV subscribers. Implementing a customized shopping model like mobile through TV media and improving customer satisfaction will reduce customer departures and provide a more convenient shopping environment through large screens. We would like to learn about the current status and problems of T commerce broadcasting and explain some technically validated models (channel-in-channel, AI speaker) and talk about improvement of legal (broadcasting and Internet multimedia business law) constraints.

• Human Ecology Research
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• v.61 no.1
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• pp.15-28
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• 2023

The aim of this study was to develop, implement, and evaluate the use of Artificial Intelligence in the prevention of violence among middle-school students. The sample for this study consisted of 20 first-year middle-school students who participated in theme selection activities in a free semester program as part of their home economics studies. The data for the study consisted of nine class observation logs, four group activity outputs, 30 class results, an online survey, and in-depth interviews with three students. A program called "R.U.OK" was developed by setting problematic situation for school violence prevention linked to the contents of the Home Economics Education(HEE) curriculum. After the program was implemented, the survey on the students' class satisfaction content elements, with AI-based learning activities and PBL and interest, displayed high points, with an average of 4.0 or higher. Our qualitative analysis produced four significant results. First, students' concerns about school violence had increased and they showed a change in attitude, having more empathy with friends and more interest in their surroundings. Second, digital and AI literacy had improved, and students' interest in digital media learning had increased. Third, there had been an improvement in problem-solving ability in terms of being able to think more critically and independently. Fourth, the results also demonstrated that there had been a positive effect on self-direction and an improved capacity for teamwork. This study was significant in demonstrating the effectiveness of a program for the prevention of school violence based on the use of digital technology in the educational environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.17-27
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• 2024

Video frame interpolation is an important technique used in the field of video and media, as it increases the continuity of motion and enables smooth playback of videos. In the study of video frame interpolation using deep learning, Kernel Based Method captures local changes well, but has limitations in handling global changes. In this paper, we propose a new U-Net structure that applies feature map differentiation and two directions to focus on capturing major changes to generate intermediate frames more accurately while reducing the number of parameters. Experimental results show that the proposed structure outperforms the existing model by up to 0.3 in PSNR with about 61% fewer parameters on common datasets such as Vimeo, Middle-burry, and a new YouTube dataset. Code is available at https://github.com/Go-MinSeong/SF-AdaCoF.