• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.102-104
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• 2000

우리나라의 지번체계는 인구증가와 경제발전에 따른 도시의 팽창과 각종 개발사업에 따른 토지의 등록, 분할 및 합병 등이 빈번하게 발생함에 따라서 불규칙하고 불합리하게 지번이 부여되어 왔다. 이에 따라 선진국과 같은 체계적이고, 합리적인 주소체계의 필요성이 꾸준히 대두되어 왔으며 정확성, 일관성, 유통성, 경제성, 검색성 등의 여러 가지 특성을 갖출 수 있는 도로방식에 의한 도로명과 건물번호를 체계적으로 부여하는 새로운 주소체계를 도입하기로 하였다. 이러한 이유로 필요성이 높아지고 있는 새주소 관리 시스템은 크게 4개의 부시스템으로 나누어지는데 본 논문에서는 이러한 4개의 부시스템 중에서 도로 관리 시스템 대하여 새주소 부여 체계와 원칙에 따라 설계하고 MAPINFO 와 MAPBASIC 언어를 이용하여 구현한다.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.471-473
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• 2011

도로경계석과 측구는 차도와 인도의 구분에 의해 주행하는 차량으로부터 인도의 보행자를 보호하고 강우 시 노면의 배수를 용이하게 하기 위한 도로시설물로서 그의 시공품질은 보행자의 안전은 물론 도시미관의 측면에서 매우 중요하다. 그러나 기존의 시공법은 콘크리트를 몇 회로 나누어 분할 타설하는 방법을 사용함으로써 시공불량에 의해 도로경계석이 원 위치에서 이탈하거나 기울어지는 사례가 종종 발생하고 있다. 이러한 문제점들을 해결하고 시공품질을 높이기 위하여 도로경계석과 측구의 일체형 시공법으로써 거푸집 레일과 두 가지 형태의 지그를 사용하는 방법이 최근 제안되었으며 본 연구에서는 새로운 표면처리장치의 개발에 의해 콘크리트 측구의 표면처리방법을 개선하고자 한다.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1369-1382
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• 2005

Vehicle segmentation, which extracts vehicle areas from road scenes, is one of the fundamental opera tions in lots of application areas including Intelligent Transportation Systems, and so on. We present a vehicle segmentation approach for still images captured from outdoor CCD cameras mounted on the supporting poles. We first divided the input image into a set of two-dimensional grids and then calculate the feature values of the edges for each grid. Through analyzing the feature values statistically, we can find the optimal rectangular grid area of the vehicle. Our preprocessing process calculates the statistics values for the feature values from background images captured under various circumstances. For a car image, we compare its feature values to the statistics values of the background images to finally decide whether the grid belongs to the vehicle area or not. We use dynamic programming technique to find the optimal rectangular gird area from these candidate grids. Based on the statistics analysis and global search techniques, our method is more systematic compared to the previous methods which usually rely on a kind of heuristics. Additionally, the statistics analysis achieves high reliability against noises and errors due to brightness changes, camera tremors, etc. Our prototype implementation performs the vehicle segmentation in average 0.150 second for each of $1280\times960$ car images. It shows $97.03\%$ of strictly successful cases from 270 images with various kinds of noises.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.33-35
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• 2012

위치 기반 서비스에 대한 활용이 증가되면서 도로 네트워크 환경에서 차량의 이동 궤적을 통해 밀집구역을 발견하기 위한 연구들이 진행되고 있다. 기존 연구들은 특정 도로 세그먼트에 포함된 차량 수를 고려하여 밀집 구역을 발견하였다. 하지만 실제적인 도로 환경에서는 도로마다 다른 길이나 도로의 폭이 다르기 때문에 차량 수만으로 도로가 밀리는 구간을 발견하기에는 문제가 있다. 또한 기존 밀집 구역 발견 연구들은 도로 내 방향성을 고려하지 않는 밀집 구간을 발견한다. 따라서 본 논문에서는 기존 밀집도 기반 클러스터링 연구와는 달리 도로 내 차량 및 도로 환경을 고려하여 도로 혼잡 구간을 판별하는 기법을 제안한다. 제안하는 혼잡 구간 판별 기법은 도로 네트워크를 거리와 폭이 다른 세그먼트로 분할하여 방향성이 존재하는 각 도로 내에 차량의 속도 및 객체 포화도에 따른 혼잡 세그먼트를 추출하고, 이를 통해 혼잡 구간을 판별하는 클러스터를 수행한다. 성능 평가 결과를 통해 제안하는 기법은 혼잡 구역을 클러스터링하여 방향 별 혼잡 구간을 파악할 수 있음을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.77-85
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• 2011

In this paper, we propose an region segmentation and texture based feature extraction method which split the pavement and roadway from the camera which equipped to the visually impaired person during a walk. We perform the hough transformation method for detect the boundary between pavement and roadway, and devide the segmented region into 3-level according to perspective. Next step, split into pavement and roadway according to the extracted texture feature of segmented regions. Our walking assistance system use rotation-invariant LBP and GLCM texture features for compare the characteristic of pavement block with various pattern and uniformity roadway. Our proposed method show that can segment two regions with illumination invariant in day and night image, and split there regions rotation and occlution invariant in complexed outdoor image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.285-292
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• 2013

This study proposes a network matching method based on a network division technique. The proposed method generates polygons surrounded by links of the original network dataset, and detects corresponding polygon group pairs using a intersection-based graph clustering. Then corresponding sub-network pairs are obtained from the polygon group pairs. To perform the geometric correction between them, the Iterative Closest Points algorithm is applied to the nodes of each corresponding sub-networks pair. Finally, Hausdorff distance analysis is applied to find link pairs of networks. To assess the feasibility of the algorithm, we apply it to the networks from the KTDB center and commercial CNS company. In the experiments, several Hausdorff distance thresholds from 3m to 18m with 3m intervals are tested and, finally, we can get the F-measure of 0.99 when using the threshold of 15m.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.75-82
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• 2021

The number of traffic accidents is about 230,000, and due to non-recurring congestion and high driving speed, the number of deaths per traffic accident on freeways is more than twice compared to other roads. Currently, traffic information is provided based on nodes and links using the centerline of the road, but it does not provide detailed speed information. Recently, installing sensors for vehicles to monitor obstacles and measure location is becoming common not only for autonomous vehicles but also for ordinary vehicles as well. The analysis using large-capacity location-based data from such sensors enables real time service according to processing speed. This study presents an mapping method for individual vehicle data analysis based on space. The processing speed of large-capacity data was increased by using method which applied a quaternary notation basis partition method that splits into two directions of longitude and latitude respectively. As the space partition was processed, the average speed was similar, but the speed standard deviation gradually decreased, and decrease range became smaller after 9th partition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.53-62
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• 2013

This paper proposes a method of automatically predicting the vanishing point for the purpose of detecting the road region from natural images. The proposed method stably detects the vanishing point in the road environment by analyzing the dominant orientation of the image and predicting the vanishing point to be at the position where the feature components of the image are concentrated. For this purpose, in the first stage, the image is partitioned into sub-blocks, an edge sample is selected randomly from within the sub-block, and RANSAC is applied for line fitting in order to analyze the dominant orientation of each sub-block. Once the dominant orientation has been detected for all blocks, we proceed to the second stage and randomly select line samples and apply RANSAC to perform the fitting of the intersection point, then measure the cost of the intersection model arising from each line and we predict the vanishing point to be located at the average point, based on the intersection point model with the highest cost. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for detecting the vanishing point.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.94-102
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• 2012

Recently entered into an aging socity as the number of elderly drivers is increasing. Traffic accidents of elderly drivers are caused by driver inattentions such as poor vehicle control due to aging, visual information retrieval problems caused by presbyopia, and objects identifying problems caused by low contrast sensitivity. In this paper, detection method of ROIs on the road is proposed. The proposed method creates the saliency map to detect the candidate ROIs from the input image. And, the input image is segmented to obtain the ROIs boundary. Finally, selective visual attention regions are detected according to the presence or absence of a segmented region with saliency pixels. Experimental results from a variety of outdoor environmental conditions, the proposed method presented a fast object detection and a high detection rate.