• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.46-53
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• 2008

The ITS(Intelligent Transport System) enables us to provide solutions on traffic problems, while maximizing safety and efficiency of road and transportation systems, by combining technologies from information and communication, electrical engineering, electronics, mechanics, control and instrumentation with transportation systems. The issues that an integration system for massive traffic data sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources. In this paper, we describe how to extract and load of the heterogeneous massive traffic data from the operational databases, such as FTMS and ARTIS using commercial tools. Also, we experiment on traffic data warehouses with integrated quality management techniques for providing high quality data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.36-45
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• 2009

Recently, there are interested in the automatic traffic flowing and accident detection using various low level information from video in the road. In this paper, the automatic traffic flowing and algorithm, and application of traffic accident detection using traffic management systems are studied. To achieve these purposes, the spatio-temporal relation models using topological and directional relations have been made, then a matching of the proposed models with the directional motion verbs proposed by Levin's verbs of inherently directed motion is applied. Finally, the synonym and antonym are inserted by using WordNet. For the similarity measuring between proposed modeling and trajectory of moving object in the video, the objects are extracted, and then compared with the trajectories of moving objects by the proposed modeling. Because of the different features with each proposed modeling, the rules that have been generated will be applied to the similarity measurement by TSR (Tangent Space Representation). Through this research, we can extend our results to the automatic accident detection of vehicle using CCTV.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.95-104
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• 2012

Pattern recognition using ensemble classifiers is composed of strong classifier which consists of many weak classifiers. In this paper, we used feature extraction to organize strong classifier using static camera sequence. The strong classifier is made of weak classifiers which considers environmental factors. So the strong classifier overcomes environmental effect. Proposed method uses binary foreground image by frame difference method and the boosting is used to train crowdedness model and recognize crowdedness using features. Combination of weak classifiers makes strong ensemble classifier. The classifier could make use of potential features from the environment such as shadow and reflection. We tested the proposed system with road sequence and subway platform sequence which are included in "AVSS 2007" sequence. The result shows good accuracy and efficiency on complex environment.

• Spatial Information Research
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• v.20 no.6
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• pp.45-57
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• 2012

The street trees in the urban areas provide an comfortable environment to the pedestrians and drivers and play important roles to absorb the carbons. Therefore, it is necessary to acquire and manage efficiently the location, height, and crown width of street trees. This study suggests a methodology to provide quantitative information of the street trees in urban areas including the quantity, location, height, and crown width of the trees. Therefore, it is more appropriate to add functionality of changing size of the crown width of the trees in the method. In addition, the positions of the street trees were selected using the fact that street trees are generally planted along the road in a straight line. An experiment on extracting street trees was conducted in parts of Osan-si, Gyeonggi-do and the suitability of the suggested methodology was evaluated by comparing the results to a 1/1,000 digital map. Through the experimental results, the minimum, maximum, and the root mean square errors of the position of street trees were 0.5m, 1.9m, and approximately ${\pm}0.4m$, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.104-114
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• 2016

Potholes have caused diverse problems such as wheel damage and car accident. A pothole detection technology is the most important to provide efficient pothole maintenance. The previous pothole detections have been performed by manual reporting methods. Thus, the problems caused by potholes have not been solved previously. Recently, many pothole detection systems based on video cameras have been studied, which can be implemented at low costs. In this paper, we propose a new pothole detection algorithm based on saliency map information in order to improve our previously developed algorithm. Our previous algorithm shows wrong detection with complicated situations such as the potholes overlapping with shades and similar surface textures with normal road surfaces. To address the problems, the proposed algorithm extracts more accurate pothole regions using the saliency map information, which consists of candidate extraction and decision. The experimental results show that the proposed algorithm shows better performance than our previous algorithm.

• 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 KIISE:Software and Applications
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• v.26 no.10
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.32-39
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• 2016

Intelligent Transport Systems (Intelligent Transportation System: ITS) is a system for inducing a flow of ideal car for using the most advanced technology, it is determined the status of the road, and take appropriate action. In order to be measured at various time points, and is managed, the information about the traffic situation is used image using a computer mainly. The image processing using a computer, it is an easy way to collect parameters of the various traffic in real time, technology has developed more and more. Vehicle detection of transport parameters of intelligent transportation system is a very important technology basically. Therefore, technology detection method using car background images and the contour line extraction method using an edge is used, however, problems have been raised on the accuracy of the detection rate.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.16-26
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• 2012

A path generation method considering the tendencies of users is required because users has the preferred routes through the properties such as time, distance, and congest. The existing method must input the additional information about user's tendency to generate path considering moving tendencies. However, mobile device have the inconvenient interface. Therefore users are reluctant to input additional information for path generation. In this paper, we propose the path generation method considering the tendencies of users without additional input of users tendencies. To show the superiority of the proposed method, we compared it with shortest distance or time path through performance evaluation.