• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.704-713
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• 2016

Recently, the value and the status of the body that are due to the mobile wearable device have attracted attention globally confusing in media convergence age. The mobile wearable device connect worldwide that are networked to overcome the limitations of space and time, also it provide a great opportunity to be able to us to open a new cultural experience and value to the populace. In this study, spatial information acquired by the virtual reality 360 degrees camera around the Gangnam station. Also, extended spatial experience performed by use of virtual reality headset, smart phone connected to the Youtube platform. Sense of body can also lose its value due to voyeurism, it is facing the transformational period in civilization. In conclusion, through deep research into the intersections of technology and a human being for the future, maintaining the proper balance is the key. Therefore, this study identified the need for interdisciplinary research.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.239-245
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• 2020

Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.211-218
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• 2018

Recently, various technologies related to IIoT are introduced continuously due to the spread of IoT and smart factory industries. This paper proposes the construction of a two-way wireless network system for monitoring plant equipment using these various technologies. The main technologies used in this thesis are design techniques for micro sensor nodes to monitor facility conditions at various sites, MQTT technology for wireless communication between local server and sensor nodes and Node-RED based design technologies, which store data collected and can be easily presented to users via wired and wireless wires. In addition, a wireless two-way camera system was also implemented in which the screen images of the site can be viewed in the situation room according to the instructions of the situation room when determining abnormal conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.75-83
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• 2007

A model based approach of tracking the limbs of a walking human subject is proposed in this paper. The tracking process begins by building a data base composed of conditional probabilities of motions between the limbs of a walking subject. With a suitable amount of video footage from various human subjects included in the database, a probabilistic model characterizing the relationships between motions of limbs is developed. The motion tracking of a test subject begins with identifying and tracking limbs from the surveillance video image using the edge and silhouette detection methods. When occlusion occurs in any of the limbs being tracked, the approach uses the probabilistic motion model in conjunction with the minimum cost based edge and silhouette tracking model to determine the motion of the limb occluded in the image. The method has shown promising results of tracking occluded limbs in the validation tests.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.209-217
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• 2012

In this paper, I proposed new system that a user makes modeling 3D symmetric pottery using mouse and can confirm the result immediately in internet browser. The main advantage of proposed system is that users who have no specialized knowledge about 3D graphic can easily create 3D objects. And a user can use it that has only PC connected network and mouse without additional devices as like expensive haptic and camera device. For developing proposed system, VRML/X3D that is International Standard language for virtual reality and 3D graphics was used. Because it was born based on internet that is different from other 3D graphic languages, it was able to interact and navigate with users. With those features and high completeness of 3D pottery realization using mouse considered, the system may be useful and is superior in performance to other pottery modeling system.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.983-986
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• 2007

Concrete surface grinding is required for flatness and adhesiveness of concrete surface. The procedure is, however, labor intensive and has a hazardous work condition. Also, the productivity and the quality of concrete surface grinding depend on the levels of worker. Thus, the development of remote controlled concrete surface grinding equipment is necessary to prevent the environmental pollution and to protect the workers from hazardous work condition. However, it is difficult to evaluate the grinded surface objectively in a remote controlled system. The machine vision system developed in this study takes the images of grinded surface with the network camera for image processing. Then, by representing the quality test results to the graphic MMI program of the remote control station, the quality control system is constructed. The machine vision algorithm means the image processing algorithm of grinded concrete surface and this paper presents the objective quality control standard of grinded concrete surface through the application of the suggested algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.13-19
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• 2011

In this paper, a visual surveillance system using SOPC based NIOS II embedded processor and C2H compiler was implemented. In this system, the IP is constructed by C2H compiler for the output of the camera images, image processing, serial communication and network communication, then, it is implemented to effectively control each IP based on the SOPC and the NIOS II embedded processor. And, an algorithm which updates the background images for high speed and robust detection of the moving objects is proposed using the Adaptive Gaussian Mixture Model(AGMM). In results, it can detecte the moving objects(pedestrians and vehicles) under day-time and night-time. It is confirmed that the proposed AGMM algorithm has better performance than the Adaptive Threshold Method(ATM) and the Gaussian Mixture Model(GMM) from our experiments.