• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.287-293
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• 2023

With the rapid development of domestic and international over-the-top markets, a large amount of video content is being created. As the volume of video content increases, consumers tend to increasingly check data concerning the videos before watching them. To address this demand, video summaries in the form of plot descriptions, thumbnails, posters, and other formats are provided to consumers. This study proposes an approach that automatically generates posters to effectively convey video content while reducing the cost of video summarization. In the automatic generation of posters, face recognition and clustering are used to gather and classify character data, and keyframes from the video are extracted to learn the overall atmosphere of the video. This study used the facial data of the characters and keyframes as training data and employed technologies such as DreamBooth, a text-to-image generation model, to automatically generate video posters. This process significantly reduces the time and cost of video-poster production.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.773-793
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• 2022

The moving picture experts group (MPEG) immersive video (MIV) coding standard has been established to support six degrees of freedom (6DoF) in virtual reality (VR) by transmitting high-quality multiple immersive videos. The MIV exploits two approaches considering tradeoff between bandwidth and computational complexity: 1) eliminating correlation between multi-view videos or 2) selecting representative videos. This paper presents performance analysis on intermediate synthesized views on source view positions and synthesized pose traces using high-efficiency video coding (HEVC) and versatile video coding (VVC) for above-mentioned two approaches.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.439-445
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• 2022

Due to the development of online media platforms and the COVID-19 incident, the mass production and consumption of digital video content are rapidly increasing. In order to select digital video content, users grasp it in a short time through thumbnails and teaser videos, and select and watch digital video content that suits them. It is very inconvenient to check all digital video contents produced around the world one by one and manually edit teaser videos for users to choose from. In this paper, keyframes are extracted based on YCbCr color models to automatically generate teaser videos, and keyframes extracted through clustering are optimized. Finally, we present a method of producing a teaser video to help users check digital video content by connecting the finally extracted keyframes.

• Journal of Korean Biological Nursing Science
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• v.17 no.1
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• pp.19-27
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• 2015

Purpose: The aim of this study was to identify the effect of video programs of cardiopulmonary cerebral resuscitation (CPCR) education of cardiopulmonary cerebral resuscitation of nurses. Methods: The subjects of the study were 64 nurses working in a university hospital. Nurse's CPCR performance have been measured four times (pre-test, post-test at immediately, 3 months and 6 months after intervention). Data were collected from February to August 2013. Results: There were significant differences in knowledge, attitude, self-efficacy, and performance between groups by measure time. And there were significant interactions in knowledge, self-efficacy, and performance between groups, within groups, except for the attitude. The video programs of CPCR interventions appear to be effective in the improvement of knowledge, self-efficacy, and performance, as compared to the control group. Conclusion: The video programs of CPCR education was an effective intervention to improve and retain the level of knowledge, attitude, self-efficacy and performance. And the video program of CPCR education have an advantage of self-learning effect for nurses with shift work. Therefore video programs of CPCR education will be utilized for continuing nurse's education.

• Journal of Science Education
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• v.45 no.1
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• pp.118-128
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• 2021

In this study, as the viewing characteristics of users of YouTube video content related to science education, 'Inflow and Access' is analyzed to find out the interaction between learners and the system, and 'Reaction and Subscription' to find out the interaction between learners and contents. To this end, the YouTube channel "Elementary Science TV," was selected as the subject of research. The channel is mainly focused on the contents of elementary science textbooks, STEAM, and gifted education. The channel's data of YouTube studio was analyzed. The following results were obtained through data analysis: first, as a result of 'Inflow and Access' analysis, YouTube video content related to science education was most often introduced through external links, and the access device was mainly a computer. Second, as a result of the analysis of 'Reaction and Subscription,' 'like' and commenting performed as a reaction to the video were less than 1% of the number of views. Most users watch without a subscription, and watch for longer when using self-directed. Although this study was analyzed through a limited channel called 'Elementary Science TV,' we were able to discover a little about the users' viewing characteristics of YouTube video contents related to science education. In the future, it is expected that it can be used as a basic material for creating videos related to science education for remote classes, establishing a science education video platform.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.29-34
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• 2021

The real-time video education is used as an effective method of operating classes that replaces face-to-face education of instructors and learners in remote areas. However, the existing video call and video conferences system is mainly used, and this is effective in linguistic education because it focuses on lecture through video, but it is not utilized in other education. In this paper, we propose a design model of real-time video system that can improve the effectiveness of science curriculum and mathematics education by providing the functions that can be utilized during class by improving limitations of image - oriented image education.

• Journal of Korean Clinical Health Science
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• v.1 no.1
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• pp.19-27
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• 2013

Purpose. To investigate the effects of a video education program on anxiety, pain, adherence to self care of knee arthroscopy surgery. Methods. The data were collected from July to October 2012. The participants were 25 people in the experimental group and 25 in the control group. The video education, which was 30 minutes in length, was shown before operation day using a CD. Results. In the experimental group, adherence to self care increased significantly, and anxiety, VAS is not significantly compared to the control group. Conclusions. It has been confirmed that video education program has positive effects on adherence to self care for patients following arthroscopy surgery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3204-3220
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• 2021

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.283-294
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• 2004

Within the rapidly changing environment of global economics, the environment of higher education in the universities & companies, also, has been, encountering various changes. Popularization on higher education related to lifetime education system, putting emphasis on the productivity of education services and the acquisition of competitiveness through the market of open education, the breakdown of the ivory tower and the Multiversitization of universities & companies, importance of obtaining information in the universities & companies, and cooperation between domestic and oversea universities, industry and educational system must be acquired. Therefore, in order to adequately cope with these kinds of rapid changes in the education environment, operating E-Learning Education & company by utilizing various information technologies and its fixations such as Internet, E-mail. CD-ROMs. Interactive Video Networks (Video Conferencing, Video on Demand), CableTV etc., which has no time or location limitation, is needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.771-788
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• 2020

Detection of abandoned objects for smart video surveillance should be robust and accurate in various situations with low computational costs. This paper presents a new algorithm for abandoned object detection based on the dual background model. Through the template registration of a candidate stationary object and presence authentication methods presented in this paper, we can handle some complex cases such as occlusions, illumination changes, long-term abandonment, and owner's re-attendance as well as general detection of abandoned objects. The proposed algorithm also analyzes video frames at specific intervals rather than consecutive video frames to reduce the computational overhead. For performance evaluation, we experimented with the algorithm using the well-known PETS2006, ABODA datasets, and our video dataset in a live streaming environment, which shows that the proposed algorithm works well in various situations.