• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.171-178
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• 2010

The surveillance system in general, has been sufficiently studied in the field of wireless semiconductor using basic sensors and its study of image surveillance system mainly using camera as a sensor has especially been fully implemented. In this paper, we propose 'Intelligent Image Detection System' used by image object identification technique based on the result analysis of various researches. This 'Intelligent Image Detection System' can easily trace and judge before and after a particular incident and ensure affirmative evidence and numerous relative information. Therefore, the 'Intelligent Image Detection System' proposed in this paper can be effectively used in the lived society such as traffic management, disaster alarm system and etc.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.103-109
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• 2010

As color was used for formative language since a human activity was beginning, all the symptoms in the world that the human eye can see is present. In this paper, we identify Korea traditional color harmony for extracted key frames from scene change detection. Traditional color is classified as 5-Jung-color and 5-Kan-color, and determine whether to harmony. Red, blue, yellow, black, and white, called 5-Jung-color and pink, blue, purple, sulfur, and green, called the 5-Kan-color was identified. First, we extract edge using Canny algorithm. And, we are labeling and clustering colors around the edge. Finally, we identify the traditional color using identification method of traditional color harmony. The proposed study in this paper has been proven through experiments.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.433-435
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• 2020

본 논문에서 저작권 보호 기술 적용을 위해 실감형 360도 영상의 강인성 정보를 이용하여 UCI 운영을 위한 응용 메타데이터 요소를 제안한다. 오늘날 멀티미디어 콘텐츠의 산업의 규모가 비약적으로 커지고 있음에 따라 이를 효과적으로 관리 및 유통할 수 있는 콘텐츠 고유의 식별체계가 요구되고 있다. 현재 국내에서 운용 중인 대표 식별자는 정통부가 개발한 UCI가 활용되고 있다. UCI는 다양한 멀티미디어 콘텐츠를 효과적으로 관리 및 유통할 수 있으나 저작권 보호 기술에 직접적으로 연관이 되어 있지 않아 이를 보완할 수 있는 기술이 요구된다. 본 논문에서는 UCI와 직접으로 연동할 수 있는 실감형 360도 동영상 응용 메타데이터 요소 및 운영 방법을 제안하여 저작권 보호 기술을 적용할 수 있도록 한다.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.109-120
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• 2024

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.4
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• pp.333-341
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• 2008

A remote sensing technique to identify trajectories of breaking waves in the surf zone using oblique digital nearshore videos is proposed. The noise arising from white foam induced by wave breaking has hindered accurate remote sensing of wave properties in the surf zone. For this reason, this paper focuses on image processing to remove the noise and wave trajectory identification essential for wave property estimation. The nearshore video imagery sampled at 3 Hz are used, covering length scale(100 m). Original image sequences are processed through image frame differencing and directional low-pass image filtering to remove the noise characterized by high frequencies in the video imagery. The extraction of individual wave crest features is conducted using a Radon transform-based line detection algorithm in the processed cross-shore image timestacks having a two-dimensional space-time domain. The number of valid wave crest trajectories identified corresponds to about 2/3 of waves recorded by the in-situ sensors.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.123-127
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• 2020

In order to obtain the overall fire line information of medium and large forest fires at night, the ground control system was developed to determine whether forest fires occurred through real-time video clips and to calculate the location of the forest fires determined using the location of drones, angle information of video cameras, and altitude information on the map to reduce the time required for regular video matches obtained after the completion of the mission. To verify the reliability of the developed function, the error distance of the aiming position information of the flight altitude star and the image camera was measured, and the location information within the reliable range was displayed on the map. As the function developed in this paper allows real-time identification of multiple locations of forest fires, it is expected that overall fire line information for the establishment of forest fire extinguishing measures will be obtained more quickly.