• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.33-38
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• 2012

In this paper we proposed an algorithm selection method based on the features of video inputs which are acquired from the large scale integrated surveillance system. The example of integrated surveillance system is the metro railway system. Automated surveillance system at large area saves the human resource and minimizes the non-observing spots. We have analyzed the input video under this system in order to apply adequate video analytics algorithms in each installing places and for each situation. Based on the analysis, we suggested event processing scenarios and video analytic algorithm selection.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.371-374
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• 2011

Recently, retrieval of various video data has become an important issue as more and more multimedia content services are being provided. To effectively deal with video data, a semantic-based retrieval scheme that allows for processing diverse user queries and saving them on the database is required. In this regard, this paper proposes a semantic-based video retrieval system that allows the user to search diverse meanings of video data for electrical safety-related educational purposes by means of automatic annotation processing. If the user inputs a keyword to search video data for electrical safety-related educational purposes, the mobile agent of the proposed system extracts the features of the video data that are afterwards learned in a continuous manner, and detailed information on electrical safety education is saved on the database. The proposed system is designed to enhance video data retrieval efficiency for electrical safety-related educational purposes.

• Journal of the Ergonomics Society of Korea
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• v.34 no.6
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• pp.597-607
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• 2015

Objective: The purpose of this study is to identify the effects of sign language video location in e-learning system for the hearing-impaired. Background: E-learning education is a good way to resolve the inequality of education for the disabled. Providing a sign language video in e-learning education for the hearing-impaired is very important for their learning. Although the location of sign language video is an important factor in the design of the video, the effect of its location in learning using the e-learning system was not studied. Method: In order to identify the effect of sign language video location on the learning of the hearing-impaired using the e-learning system, the prototypes of the system with different locations were developed. Eighteen people with hearing impairment participated in this experiment. Learning presence, learning immersion, and learning satisfaction were used to measure learning effects with sign language video location. Results: Bottom right position was more preferred through preference evaluation for sign language video location. The learning effect with sign language video location (bottom-left and bottom-right) was not significant. That is, the effects of learning presence, immersion, and satisfaction were not statistically significant with video location. Conclusion: From this study, the following have to be considered in e-learning system design for the hearing-impaired. Although the location of a sign language video is not a significant factor from the experiment, the bottom right position in the design is proposed because learning presence and satisfaction is slightly higher at the bottom right position, and the position is preferred from subjective evaluation. From the analysis of interview data, it was also proposed that the design of a sign language video should be improved for the hearing-impaired. Application: The result of this study can be applied to the e-learning system design for the hearing-impaired.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.38-38
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• 2004

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.273-278
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• 2016

Disasters are linked with some attributes of modern society in diversity, complexity and unpredictability, so various approach and remedies of them will appease the nation's anxiety upon them. For this reason, in this paper we review the building plans and its application and its application Province pros and cons of intelligent video surveillance analysis system for social security, the future proposed service is the safety of people while performing a comprehensive urban traffic control functions as a video monitoring system for social security and to ensure, to prevent crime and accidents, it is to crack down on criminal offenses in advance expected to protect the property of citizens and public facilities.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.121-132
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• 2021

The purpose of this study is to explore ways to improve the quality of real-time video lectures in a non-face-to-face environment using IPA (Importance-Performance Analysis). Recently, due to the impact of COVID-19 in universities, all remote classes are being implemented, so research is needed to raise learner awareness. Accordingly, factor analysis, mean analysis, correspondence analysis, and IPA analysis were performed based on the data of 632 students who responded from March 21 to June 30, 2021 for learners of K University in Chungbuk. First, overall satisfaction was low compared to importance, and the difference in system perception was the largest. Second, the difference in learner perception of real-time video lectures through the IPA matrix showed that the system error and screen cutoff were the largest. Third, the difficulty of lecture content, task and test feedback, etc. are classified. Accordingly, the satisfaction of real-time video lectures in non-face-to-face environments is low, suggesting that school-level support for quality improvement to improve learner satisfaction in non-face-to-face environments and the role of instructors are needed to improve learners' academic achievement.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.77-80
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• 2002

In this paper, We have studied for guaranteeing the clear display of MPEG-2 video sequence when conduct splicing of MPEG-2 system streams. we focus on the PES domain splicing considering video sequence. And we wish to make a base on the TS or PS domain splicing considering video sequence. For that, first, we compared and analyzed problems that is raised when different two PES streams are spliced and effects that affect the video sequence. And based on this analysis, we have searched for methods that resolve the cause of problems that can be happened in the display of video sequence directly in PES domain.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.185-193
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• 1997

While close range photogrammetry has been widely applied for static deformation analysis, video cameras have many characteristics that make them the sensors of choice for dynamic analysis of rapidly changing situations. They also have limitations. The aim of this research is to explore the potential of a video system for monitoring dynamic objects. A pilot system consists of two camcorders, VCR, and PC with frame grabber. To estimte the performance of this system for moving objects, a car was imaged covering several phases when starting to drive. The sequential images of a moving car were recorded on VCR. 15 images per second were digitized in an off-line mode by frame grabber. The image coordinates of targets attached to the rear bumper of a car were acquired by IDRISI, and the object coordinates were derived based on DLT. This research suggests that home video cameras, PC, and photogrammetric principles are promising tools for monitoring of the moving objects and vibrations as well as other time dependent situations.