• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.144-146
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• 2019

Recently, according to the rapid development of surveillance information, closed-circuit television (CCTV) has become an indispensable component in security systems. A lot of advanced technologies of encryption and compression are implementing to improve the performance and security levels of the CCTV system. Especially, 360 video CCTV streaming is promising for surveillance without blind areas. However, compared to previous systems, 360 CCTV requires large bandwidth and low latency. Therefore, it requires more efficiently effort to improve the CCTV system performance. In order to meet the demands of 360 CCTV streaming, transcoding is an essential process to enhance the current CCTV system. Moreover, encryption algorithm is also an important priority in security system. In this paper, we propose a real-time transcoding solution in combination with the ARIA and AES algorithms. Experimental results prove that the proposed method has achieved around 195% speed up transcoding compared to FFMPEG libx265 method. Furthermore, the proposed system can handle multiple transcoding sessions simultaneously at high performance for both live 360 CCTV system and existing CCTV system.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.66-69
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• 2016

Recently, augmented reality and virtual reality in the ICT sector have been highlighted. So also interested in related HMD areas to facilitate contact with the VR content is being attend. This paper proposes a method for implementing to the virtual reality through the mobile HMD device with the real time 360 image. This system is required the real time 360 image streaming server configuration and image processing for augmented reality and virtual reality. The configuration of the streaming server is configured the DB server to store images and the relay server that can relay images to other devices. Augmented image processing module is composed based on markerless tracking, and there are four modules that are recognition, tracking, detecting and learning module. Also, the purpose of this paper is shown the augmented 360 image processing through the Mobile HMD.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.165-166
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• 2023

Construction inspection and monitoring are key activities in construction projects. Automation of inspection tasks improves the limitations and inefficiencies of manual construction inspections, enabling systematic and consistent construction inspections. In this paper, an RTSP (Real-Time Streaming Protocol) system is used to remotely manage and supervise the construction site without having to visit the construction site by deploying a robot on site on behalf of four construction stakeholders (owner, supervisor, constructor, and designer). I would like to propose. The proposed system can contribute to identifying and monitoring the process process and work results at the construction site in real time.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.51-57
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• 2020

Currently, High-efficient video coding (HEVC) has become the most promising video coding technology. However, the implementation of HEVC in video streaming systems is restricted by factors such as cost, design complexity, and compatibility with existing systems. While HEVC is considering deploying to various systems with different reached methods, H264/AVC can be one of the best choices for current video streaming systems. This paper presents an adaptive method for manipulating video streams using video coding on an integrated circuit (IC) designed with a private network processor. The proposed system allows to transfer multimedia data from cameras or other video sources to client. For this work, a series of video or audio packages from the video source are forwarded to the designed IC via HDMI cable, called Tx transmitter. The Tx processes input data into a real-time stream using its own protocol according to the Real-Time Transmission Protocol for both video and audio, then Tx transmits output packages to the video client though internet. The client includes hardware or software video/audio decoders to decode the received packages. Tx uses H264/AVC or HEVC video coding to encode video data, and its audio coding is PCM format. By handling the message exchanges between Tx and the client, the transmitted session can be set up quickly. Output results show that transmission's throughput can be achieved about 50 Mbps with approximately 80 msec latency.

• ETRI Journal
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• v.26 no.3
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• pp.203-217
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• 2004

This paper presents a method and architecture to analyze streaming media and multimedia conferencing traffic. Our method is based on detecting the transport protocol and port numbers that are dynamically assigned during the setup between communicating parties. We then apply such information to analyze traffic generated by the most popular streaming media and multimedia conferencing applications, namely, Windows Media, Real Networks, QuickTime, SIP and H.323. We also describe a prototype implementation of a traffic monitoring and analysis system that uses our method and architecture.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.15-31
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• 2024

Purpose - This study explores the causes behind the high rate of impulse purchases in live streaming e-commerce. Design/methodology/approach - The research model is empirically validated with survey data collected from 324 respondents in China, using the Partial Least Squares (PLS) methodology. Findings - We found that the interactivity and visibility of live streaming are key in creating para-social relationships and perceived presence, leading to customers' impulse purchases. Research implications or Originality - This study deepens the understanding of consumers' impulse purchases in live-streaming e-commerce, revealing that para-social relationships and perceived presence developed through real-time interactions can increase impulse buying. It also highlights the differences between relationship-oriented and task-oriented interactions in fostering customers' perceived presence and para-social relationships in live streaming.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4B
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• pp.227-233
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• 2007

According to the recent development of network technology, the demands of users are diversified and the needs of multimedia traffic are increasing. In general, UDP(User Datagram Protocol) traffic is used to transport multimedia data, which satisfied the real-time and isochronous characteristics. UDP traffic competes with TCP traffic and incur the network congestion. However, TCP traffic performs network congestion control but does not consider the receiver's status. Thus, it is not appropriate in case of streaming services. In this paper, we solve a fairness problems and proposed a network algorithm based on RTP/RTCP(Real-time Transport Protocol/Realtime Transport Control Protocol) in view of receiver status. The POBA(Priority Ordered Buffer Algorithm), which applies priorities in the receiver's buffer and networks, shows that it provides the appropriate environment for streaming services in view of packet loss ratio and buffer utilization of receiver's buffer compared with the previous method.