• 전기학회논문지
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• 제65권3호
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• 환경영향평가
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• 제18권6호
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• pp.387-392
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• 2009

As Our society's environmental awareness and concern the significant increases, the importance of the legal system for environmental conservation such as the Prior Environmental Review System, Environmental Impact Assessment is growing increasingly. but, still critical issues are present such as reliability. Though there could be various causes such as the system or procedures etc. Above all, basically the environmental data problem is the critical cause. Therefore, this study was trying to improve the environmental data accuracy using the high-resolution color aerial photography, LiDAR data and Object Oriented Classification method. And in this study, classification based on coverage percentage of a particular species was attempted through the multi-resolution segmentation and multi-level classification method. The classification result was verified by comparison with 11 points local survey data. All 11 points were classified correctly. And even though the exact coverage percentage of the particular species did not be measured, It was confirmed that the species was occupied similar portion. It is important that the environmental data which can be used for the conservation value assessment could be acquired.

• 전자공학회논문지
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• 제51권2호
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• pp.23-29
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• 2014

지능형 교통 시스템(Intelligent Transportation Systems)의 첨단 교통 관리 시스템(Advanced Traffic Management System)은 고화질 카메라, 고성능 레이더 센서와 같은 향상된 인프라를 통하여 도로 상의 차량 속도, 통행량, 돌발 상황 등의 교통 상황을 실시간으로 분석하며 관련 업무를 자동화하고 있다. 특히 도로 이용자의 안전을 위해서는 돌발 상황 자동 검지 및 2차 사고 방지를 위한 시스템이 필요하다. 이러한 유고 검지 및 관리 시스템에서는 CCTV 기반 영상 검지와 레이더를 이용한 물체검지가 주로 사용된다. 본 논문은 다중 감시용 카메라를 사용한 실시간 고속도로 돌발 상황 검지 시스템에서 모자이크(mosaic) 동영상을 구성하는 방법과 다양한 각도에서 촬영된 움직이는 객체를 보다 정확하게 추적할 수 있는 배경 모델링에 기반한 알고리즘을 제안하였다. 실험결과 영상검지는 레이더검지의 근거리 음영 영역과 원거리 검지한계 영역을 보완해 줄 수 있을 뿐만 아니라 악천후를 제외한 주간 검지에서 보다 나은 분류 특징들을 갖고 있음을 확인 할 수 있었다.

• 한국CDE학회논문집
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• 제13권3호
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• pp.163-174
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• 2008

In this paper, for integrating of data on car parts we model information of parts that PDM system manages. Ontology of car parts applies existing ontology mapping research to integrate into car ontology. We propose a method for semantic integration of PLM object of MEMPHIS based on the integrated ontology. Through our method, we introduce C# ontology model to apply existing C# applications with ontology. We also classify ontology integration into three through examples and explain them. While semantically integrating PLM objects based on the integrated ontology, we explain the need for change of PLM object type and describe the process of change for PLM object type by examples.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• 한국측량학회지
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• 제35권1호
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• pp.11-22
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• 2017

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.79-89
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• 2016

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• 한국컴퓨터정보학회논문지
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• 제11권2호
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• pp.25-33
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• 2006

본 논문에서는 영상을 자동적으로 객체와 비객체 영상으로 분류하는 방법을 제안한다. 객체 영상은 객체를 포함하는 영상이다. 객체는 영상의 중심 부근에 위치하고 주변 영역과는 상이한 칼라 분포를 가지는 영역들로 정의한다. 영상 분류를 위해 객체의 특징에 기반을 두고 네 가지 기준을 정의한다. 첫 번째 기준인 중심 영역의 특이성은 중심 영역과 주변 영역간의 칼라 분포의 차이를 통해 계산된다. 두 번째 기준은 영상 내의 특이 픽셀의 분산이다. 특이 픽셀은 영상의 주변영역보다 중심 부근에서 더욱 빈번하게 나타나는 상호 인접한 픽셀들의 칼라 쌍에 의해 정의된다. 세 번째 기준은 중심 객체의 평균 경계강도이다. 세 번째 기준은 분류 기준들중에서 가장 우수한 분류 성능을 나타내지만 특징값을 추출하기 위해서는 중심 객체를 추출해야 되는 많은 연산을 내포하고 있다. 이에 이와 비슷한 특성을 나타내는 네 번째 기준으로 영상 중심 영역에서의 평균 경계강도를 선택하였다. 네 번째 분류 기준은 세 번째 분류 기준에 비해 분류 성능은 조금 낮지만 빠르게 특징값을 추출할 수 있어 많은 데이터를 빠른 시간 내에 처리해야 되는 대규모 영상 데이터 베이스에 적용가능하다. 영상을 분류하기 위해 신경회로망 및 SVM을 사용하여 이들 기준들을 통합하였으며 신경회로망 및 SVM의 분류 성능을 비교하였다.