• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.56-63
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• 2003

This paper introduces a vision based autonomous mini mobile robot that is an approach to produce real autonomous vehicle. Previous autonomous vehicles are dependent on PC, because of complexity of designing hardware, difficulty of installation and abundant calculations. In this paper, we present an autonomous motile robot system that has abilities of accurate steering, quick movement in high speed and intelligent recognition as a stand-alone system using a mono camera. The proposed system has been implemented on mini track of which width is 25~30cm, and length is about 200cm. Test robot can run at average 32.9km/h speed on straight lane and average 22.3km/h speed on curved lane with 30~40m radius. This system provides a model of autonomous mobile robot adapted a lane recognition algorithm in odor to make real autonomous vehicle easily.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.89-97
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• 2002

We have developed the 45-Van system in order to maximize the interoperability of spatial data in 45(GNSS, SIIS, GIS, ITS) by sharing and providing spatial data of remote site. The 4S-Van system enables to acquisition and production of information for GIS database and the accurate position information by combining and connecting GPS/IMU, laser, CCD(charged-coupled device) image, and wireless telecommunication technology. That is, 4S-Van system measures its position and attitude using integrated GPS/IMU and takes two photographs of the front scene by two CCD cameras, analyzes position of objects by space intersection method, and constructs database that has compatibility with existing vector database system. Furthermore, infrared camera and wireless communication technique can be applied to the 4S-Van for a variety of applications. In this paper, we discuss the design and functions of 4S-Van that is equipped with GPS, CCD camera, and IMU.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.1-10
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• 2007

This paper describes the development of a network-based passenger information system(PIS) which provides the convenience of the passenger of the train and heightens the effect of the subtitle service, the advertising and the shelter guidance broadcasting against the urgent event. The existing system uses VGA signal distributor in order to broadcast information with image and subtitle and voice guidance. In this paper we improve the existing system by applying the UDP and TCP/IP protocol and use a reference clock to solve a data loss and synchronization problem which occurs in this case. We also developed an XML-based subtitle authoring tool which can edit and play the subtitles with various 3D to improve the automatic guidance broadcasting and advertisement effect according to the operation schedule of the train. The system performance was evaluated through a simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.641-647
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• 2018

Recently, CCTV, which provides video information for multiple purposes, has been transformed into an intelligent, and the range of automation applications increased using the computer vision. A highly reliable detection method must be performed for accurate recognition of pedestrians and vehicles and various methods are being studied for this purpose. In such an object detection system. In this paper, we propose a method to detect a large number of pedestrians by acquiring three characteristic information that features of color information using HSI, motion vector information and shaping information using HOG feature information of a pedestrian in a situation where a large number of pedestrians are moving. The proposed method distinguishes each pedestrian while minimizing the failure or confusion of pedestrian detection and tracking. Also when pedestrians approach or overlap, pedestrians are identified and detected using stored frame feature data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.434-442
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• 2018

In this paper, we propose a road lane color recognition method from the image obtained from a driving vehicle. In autonomous vehicle techniques, lane information becomes more important as the level of autonomous driving such as lane departure warning and dynamic lane keeping assistance is increased. In particular the lane color recognition, especially the white and the yellow lanes, is necessary technique because it is directly related to traffic accidents. In this paper, color information of lane and road area is mapped to a 2-dimensional S-color space based on lane detection. And the center of the feature distribution is obtained by using an improved mean-shift algorithm in the S-color space. The lane color is determined by using the distance between the center coordinates of the color features of the left and right lanes and the road area. In various illumination conditions, about 97% color recognition rate is achieved.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.845-850
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• 2008

In this paper, we propose a license plate locating algorithm by using SVM. Tipically, the features regarding license plate format include height-to-width ratio, color, and spatial frequency. The method is dived into three steps which are image acquisition, detecting license plate candidate regions, verifying the license plate accurately. In the course of detecting license plate candidate regions, color filtering and edge detecting are performed to detect candidate regions, and then verify candidate region using Support Vector Machines(SVM) with DCT coefficients of candidates. It is possible to perform reliable license plate location bemuse we can protect false detection through these verification process. We validate our approach with experimental results.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.

• Journal of IKEEE
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• v.22 no.4
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• pp.970-977
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• 2018

The autonomous vehicle is based on an advanced driver assistance system (ADAS) consisting of a sensor that collects information about the surrounding environment and a control module that determines the measured data. As interest in autonomous navigation technology grows recently, an easy development framework for ADAS beginners and learners is needed. However, existing development and verification methods are based on high performance vehicle simulator, which has drawbacks such as complexity of verification method and high cost. Also, most of the schemes do not provide the sensing data required by the ADAS directly from the simulator, which limits verification reliability. In this paper, we present an interactive ADAS development and verification framework using a 3D vehicle simulator that overcomes the problems of existing methods. ADAS with image recognition based artificial intelligence was implemented as a virtual sensor in a 3D car simulator, and autonomous driving verification was performed in real scenarios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.221-223
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• 2016

Currently in the automobile market, models with many convenient functions combined with IT have been released. This change has a strength that there could be many convenient and useful functions related to driving while flaws of vehicles caused by malfunctions of these electronic equipments could trigger serious incidents. Among them, the sudden unintended acceleration considered as the most serious is a serious flaw that could threaten driver's life. However, the causes for sudden acceleration incidents have not been clearly investigated with no coping measures. As manufacturers shift the responsibility to drivers' carelessness, drivers' burden is continuously increasing. Thus, this paper designed the system to cope with sudden acceleration incidents by changing conditions of controlling parts like accelerator and brake, and internal image of the driver's seat into data through the integrated module.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.311-318
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• 2021

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.