• The Journal of the Korea Contents Association
• /
• v.5 no.1
• /
• pp.73-79
• /
• 2005

Recent advances on wireless technology are enabling the design and deployment of real-time traffic in wireless LAN. Point Coordination Function (PCF) mode of Wireless LAN is defined to provide QoS of real time traffic, such as voice, video in wireless LANs. The service polling scheduling plays an important role in real-time traffic services of wireless LAN. This paper proposes a multicast polling scheme to increase the maximum number of conversations by reducing the amount of empty polls in IEEE 802.11 PCF mode. Our simulation studies show that our scheme may improve performance of the PCF in terms of the average transfer delay and packet discard ratio.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.42 no.12
• /
• pp.17-24
• /
• 2005

Traditional Internet applications such as FIP and E-mail are increasingly sharing bandwidth with newer, more demanding applications such as Web browsing, IP telephony, video conference and online games. These new applications require Quality of Service (QoS), in terms of delay, loss and throughput that are different from QoS requirements of traditional applications. Unfortunately, current Active Queue Management (AQM) approaches offer monolithic best-effort service to all Internet applications regardless of the current QoS requirements. This paper proposes and evaluates a new AQM technique, called MCDT that provides dynamic and separated buffer threshold for each Applications, those are FTP and e-mail on TCP traffic, streaming services on tagged UDP traffic, and the other services on untagged UDP traffic. Using a new QoS metric, our simulations demonstrate that MCDT yields higher QoS in terms of the delay variation and a packet loss than RED when there are heavy UDP traffics that include streaming applications and data applications. MCDT fits the current best-effort Internet environment without high complexity.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.1
• /
• pp.475-481
• /
• 2014

Recently, it is used to apply various improved methods to determine the cause of traffic accidents. However, most of vehicle black box usually start to store the video information by an event trigger in case that the impact value at that time exceeds the threshold impact value as compared the threshold impact value saved in advance with the current impact value. there are problems with above method that a lot video information should be saved in the memory card of the vehicle black box, and the user should delete the unwanted video information every time because of unclassified video store. In this paper, we propose the ontology-based context aware algorithm that the vehicle black box recognize the situation, and then remove the video data with a low weighting factor by itself for efficient memory management.

• Journal of Information Technology Applications and Management
• /
• v.11 no.4
• /
• pp.169-179
• /
• 2004

There are many inductive loop detectors under the highways in Korea. Among the other detectors, some are image detectors. Almost all image detectors are focused one or two lane of the road and are measuring traffic information. This paper proposes to an algorithm for detecting traffic information automatically from CCTV camera images installed on the highway. The information which is counted in one lane or two contains some critical errors by occlusion frequently in case of passing larger vehicles. In this paper, we use a tracking algorithm in which the detection area include all lanes, then the traffic informations are collected from the vehicles individually using difference images in this detection area. This tracking algorithm is better than lane by lane detecting algorithm. The experiment have been conducted two different real road scenes for 20 minutes. For the experiments, the images are provided with CCTV camera which was installed at Kiheung Interchange upstream of Kyongbu highway, and video recording images at Chungkye Tunnel. For image processing, images captured by frame-grabber board 30 frames per second, 640${\times}$480 pixels resolution and 256 gray-levels to reduce the total amount of data to be Interpreted.

• The KIPS Transactions:PartC
• /
• v.15C no.2
• /
• pp.87-92
• /
• 2008

This paper proposes a session control model for the P2P(Peer to Peer) IPTV(Internet Protocol Television) services and presents the IPTV session control procedures based on the proposed model. Since, while public IPTV traffic is usually processed via a separate network, P2P IPTV traffic is processed together with the conventional Internet access traffic, the P2P IPTV control mechanism needs to provide multi-stream processing for the constituent TPS(Triple Play Service) traffic and corresponding QoS(Quality of Service) control functions. Besides, P2P IPTV session control mechanism should provide appropriate multicast control functions in order to support effective transmission of video traffic generated by personal IPTV broadcasters. The P2P IPTV session control model proposed in this paper is designed to be based on the standard SIP(Session Initiation Protocol), IGMP(Internet Group Management Protocol), and COPS(Common Open Policy Service) protocol so that it can contribute to the easy and prompt deployment of inter-operable P2P IPTV platform.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.27-30
• /
• 2000

In this paper, we propose a new dynamic bandwidth allocation and call admission control method for the VBR video sources with QoS constraints to provide user's quality of service requirements and at the same time to achieve an efficient resource management in networks. The proposed mechanism dynamically adjusts the necessary bandwidth by the networks based on the provided quality of service satisfaction degree of each connection in respect to the user's requirements in terms of loss ratio and average delay Simulation results show that our proposed dynamic method is able to provide the desired level of quality of service and high utilization.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.6
• /
• pp.182-194
• /
• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1243-1244
• /
• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.5
• /
• pp.83-99
• /
• 2021

With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.19-24
• /
• 2019

The development of ICT technology has a huge impact on the existing closed CCTV security equipment market. With the importance of video data particularly highlighted in areas such as self-driving cars, unmanned vehicles and smart cities, various technologies using video are emerging. In this paper, we proposed a method to transmit videos and metadata as a part of smart city integration, and to solve the traffic, environment and security problems caused in urban life by utilizing the metadata instead of using CCTV videos for simple recording purposes, and reverse tunneling technique was designed and implemented as a method for accessing CCTV videos for vehicles from remote locations. Integrated management of CCTV videos and metadata for vehicles that have been used only for limited purposes in closed environments will enable efficient operation of integrated centers in real time required by smart cities, such as vehicle status check, road conditions and facility management.