• Journal of Korean Society of Forest Science
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• v.94 no.2 s.159
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• pp.112-116
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• 2005

This study was conducted to obtain the information of distribution, protection and management for mammal species in fragmented forest areas around the road from June 2002 to May 2003 in 9 study sites of Baekdugdaegan mountains, Korea. Field signs of twelve mammals, moles Molera robusta, Korean hares Lepus coreanus, Manchurian chipmunk Tamias sibiricus, red squirrels Sciurus vulgaris, Korean racoon dogs Nyctereutes procyonoides, Siberian weasels Mustela sibirica, badgers Meles meles, otters Lutra lutra, Bengal cats Felis bengalensis, wild boars Sus scrofa, water deer Hydropotes inermis and roe deer Capreolus pygargus were recorded in this study. There were no differences in total number of species between 50 m areas and 50-100 m areas from road in snow and non-snow season. Number of mammals' field signs were different in non-snow season between both areas. Red squirrels and Siberian weasels were more abundant in 50 m areas, and Korean hares and Manchurian chipmunks were in 50-100 m areas. Habitat using pattern of mammal species may be affected by the road. Reasonable road construction and maintenance would be needed for protection and management of wildlife and their habitats.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.241-250
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• 2019

Road signs are guide facilities for road users, and the Ministry of Land, Infrastructure and Transport has established and operated a system to enhance the convenience of managing these road signs. The role of road signs will decrease in the future autonomous driving, but they will continue to be needed. For the accurate mechanical recognition of texts on road signs, automatic road sign recognition equipment has been developed and it has applied image-based text recognition technology. Yet there are many cases of misrecognition due to irregular specifications and external environmental factors such as manual manufacturing, illumination, light reflection, and rainfall. The purpose of this study is to derive location-based destination names for finding misrecognition errors that cannot be overcome by image analysis, and to improve the automatic recognition of road signs destination names by using Levenshtein similarity verification method based on phoneme separation.

• Journal of KIISE
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• v.42 no.2
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• pp.203-212
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• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.41-51
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• 2010

In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.13-24
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• 2008

A next generation video based car navigation is researched to supplement the drawbacks of existed 2D based navigation and to provide the various services for safety driving. The components of this navigation system could be a load object database, identification module for load lines, and crossroad identification module, etc. In this paper, we proposed the traffic lights and road sign recognition method which can be effectively exploited for crossroad recognition in video-based car navigation systems. The method uses object color information and other spatial features in the video image. The results show average 90% recognition rate from 30m to 60m distance for traffic lights and 97% at 40-90m distance for load sign. The algorithm also achieves 46msec/frame processing time which also indicates the appropriateness of the algorithm in real-time processing.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.1-10
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• 2023

With the advent of the 4th industrial revolution era, interest in autonomous driving technology is increasing. Accordingly it is necessary to seek safe driving by recognizing surrounding situations using sensors attached to autonomous vehicles along with the applicability of existing traffic facilities to autonomous driving lanes and the utilization of HD maps. In this study, in order to deduce the role of sensor only physical facilities which recognized through a laser scanner on an autonomous vehicle developed to improve road and traffic infrastructure, through comparative analysis with existing road facilities such as road signs, safety signs, and gaze guidance facilities. Sign facilities can promote driving safety by allowing autonomous vehicles to perform specific actions directly. In order to promote safe driving by recognizing sign facilities by using sensors for autonomous vehicles, it is necessary to prepare standards for installation, management, and use, and it is considered that management and supervision should be carried out continuously according to the standards.

• 한국도로학회:학술대회논문집
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• 2006.10a
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• pp.387-394
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• 2006

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.443-450
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• 2007

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.71-80
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• 2011

Almost all Drivers who are not familiar with local areas usually rely on road signs equipped along the roadways. The road signs in Korea present the name of the city along the driver’s direction. The consistency of guided name on the road-signs is important for drivers. The discordances among road signs frustrate drivers particularly when the drivers are confused with whether or not they are in the right direction. There are several studies focusing on the continuity of information on the road signs. Most of the researches, however, do not suggest the objective way but diagnose present problem. Applying the Analytic Hierarchy Process (AHP), we evaluate the impact of information on road signs and select the candidate information considering the score and limited number of information. We also suggest the reasonable spatial influence area of road sign information using geo-spatial analysis. From this study, we expect that the director in charge of selecting information can make decision reasonably without difficulties of choosing information.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.