• 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.

• Smart Media Journal
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• v.5 no.3
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• pp.93-98
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• 2016

The literal meaning of loitering is "to lingering aimlessly or as if aimless in or about a place". And most criminals show this kind of act before they actually commit crime. Therefore, detecting this kind of loitering can effectively prevent a variety of crime. In this paper, we propose a loitering-detection algorithm using the Raspberry Pi. Proposed algorithm uses an adaptive difference image to detect moving objects and morphology opening operation to enhance the accuracy of detection. The loitering- behavior is being detected by using the center of gravity of the object to see the changes of angle; and pixel movement distance to determine the height of the object. When the loitering-behavior is detected, it outputs the alarm to tell the users by using the Raspberry Pi.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1138-1145
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• 2022

While pursuing digitalization and paperless projects, the construction industry needs to settle on how to make the most of digitized data and information. On-site workers, who currently rely on paper documents to check and review design and construction plans, will need alternative ways to efficiently access the information without using any paper. Augmented Reality is a potential solution where the information customized to a user is aligned with the physical world. This paper proposes the Augmented Reality framework to deliver the information on on-site resources (e.g., workers and equipment) using head-mounted devices. The proposed framework was developed by interoperating Augmented Reality-supported devices and a digital twin platform in which all information related to ongoing tasks is accumulated in real-time. On-site resources appearing in the user's field of view are automatically detected by an object detection algorithm and then assigned to the corresponding information by matching the data in the digital twin platform. Preliminary experiments show the feasibility of the proposed framework. Worker detection results can be visualized on HoloLens 2 in near real-time, and the matching process obtained the accuracy greater than 88%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.197-205
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• 2006

This paper presents the moving object tracking method using vision system. In order to track object in real time, the image of moving object have to be located the origin of the image coordinate axes. Accordingly, Fuzzy Control System is investigated for tracking the moving object, which control the camera module with Pan/Tilt mechanism. Hereafter, so the this system is applied to mobile robot, we design and implement image processing board for vision system. Also fuzzy controller is implemented to the StrongArm board. Finally, the proposed fuzzy controller is useful for the real-time moving object tracking system by experiment.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.521-527
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• 2009

This paper suggests a collision techniques for a higher reality in augmented reality by processing collision between a real object obtained through video frame input and a marker-based virtual object or a virtual object from Opengl. Augmented reality system is providing the visual information containing a virtual object added to the real environment and interactive interface between objects and between user and objects becomes a more concerning interest. But the collision problem is essential to the interactive interface and has to be solved first. Therefore, the proposed system suggests a solution for it to increase the realism and the immersion by validating the collision among a marker-based object, a virtual object from Opengl, and a real object obtained through web camera, that is, video frame.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.80-83
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• 2005

A binocular robot vision system having an autonomously moving active viewpoint is proposed. By using this active viewpoint, the system constructs a correspondence between the images of a feature points on the right and left retinas and calculates the spatial coordinates of the feature points. The system incorporates a function of detecting straight lines in an image. To detect lines the system uses Hough transform. The system searches a region surrounded by 4 straight lines. Then the system recognizes the region as a quadrangle. The system constructs a correspondence between the quadrangles in the right and left images. By the use of the result of the constructed correspondence, the system calculates the spatial coordinates of an object. An experiment shows the effect of the line detection using Hough transform, the recognition of the surface of the object and the calculation of the spatial coordinates of the object.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.185-197
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• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1284-1292
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• 2015

As visual surveillance systems become more and more common in human lives, approaches based on these systems to solve security problems in practice are boosted, especially in railway applications. In this paper, we first propose a robust snag detection algorithm and then present a railway security system by using a combination of multiple sensors and the vision based snag detection algorithm. The system aims safety at several repeatedly occurred situations including slope protection, inspection of the falling-object from bridges, and the detection of snags and foreign objects on the rail. Experiments demonstrate that the snag detection is relatively robust and the system could guarantee the security of the railway through these real-time protections and detections.