• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.205-215
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• 2012

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.2
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• pp.98-105
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• 2007

In this paper, we propose the method and system which determines the condition for safe and unsafe lane changing. To determine the condition, first, the system sets up the Region of Interest(ROI) on the neighboring lane. Second, a dangerous vehicle is extracted during the line changing. Third, the condition is determined to wm or not by calculating the moving direction, relative distance md relative velocity. To set up the ROI, the only one side lane is detected and the interested region is expanded. Using the coordinate transformation method, the accuracy of the ROI raised. To correctly extract the vehicle on the neighboring lane, the Adaptive Background Update method and Image Segmentation method which uses the feature of the travelling road are used. The object which is extracted by the dangerous vehicle is calculated the relative distance, the relative velocity and the moving average. And then in order to ring, the direction of the vehicle and the condition for safe and unsafe is determined. As minimizes the interested region and uses the feature of the travelling road, the computational quantity is reduced and the accuracy is raised and a stable result on a travelling road images which demands a high speed calculation is showed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3086-3103
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• 2017

Video images captured by vehicle cameras often contain blurry or dithering frames due to inadvertent motion from bumps in the road or by insufficient illumination during the morning or evening, which greatly reduces the perception of objects expression and recognition from the records. Therefore, a real-time electronic stabilization method to correct fuzzy video from driving recorders has been proposed. In the first stage of feature detection, a coarse-to-fine inspection policy and a scale nonlinear diffusion filter are proposed to provide more accurate keypoints. Second, a new antiblurry binary descriptor and a feature point selection strategy for unintentional estimation are proposed, which brought more discriminative power. In addition, a new evaluation criterion for affine region detectors is presented based on the percentage interval of repeatability. The experiments show that the proposed method exhibits improvement in detecting blurry corner points. Moreover, it improves the performance of the algorithm and guarantees high processing speed at the same time.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1191-1202
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• 1999

We propose an efficient vehicle detection and classification algorithm for an electronic toll collection using the feature which is robust to abrupt intensity change between consecutive frames. The local correlation coefficient between wavelet transformed input and reference images is used as such a feature, which takes advantage of textural similarity. The usefulness of the proposed feature is analyzed qualitatively by comparing the feature with the local variance of a difference image, and is verified by measuring the improvements in the separability of vehicle from shadowy or shadowless road for a real test image. Experimental results from field tests show that the proposed vehicle detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

• Journal of IKEEE
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• v.18 no.4
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.221-228
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• 2006

In this study, an integrated 3D modeling and rendering system dealing with 3D urban landscape features such as terrain, building, road and user-defined geometric ones was designed and implemented using $OPENGL\;{|}\;ES$ (Embedded System) API for mobile devices of PDA. In this system, the authoring functions are composed of several parts handling urban landscape features: vertex-based geometry modeling, editing and manipulating 3D landscape objects, generating geometrically complex type features with attributes for 3D objects, and texture mapping of complex types using image library. It is a kind of feature-based system, linked with 3D geo-based spatial feature attributes. As for the rendering process, some functions are provided: optimizing of integrated multiple 3D landscape objects, and rendering of texture-mapped 3D landscape objects. By the active-synchronized process among desktop system, OPENGL-based 3D visualization system, and mobile system, it is possible to transfer and disseminate 3D feature models through both systems. In this mobile 3D urban processing system, the main graphical user interface and core components is implemented under EVC 4.0 MFC and tested at PDA running on windows mobile and Pocket Pc. It is expected that the mobile 3D geo-spatial information systems supporting registration, modeling, and rendering functions can be effectively utilized for real time 3D urban planning and 3D mobile mapping on the site.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.585-601
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• 2004

The purpose of this paper is to examine the change of Korean land transportation system and pattern during 1905-1911 concentrated on road construction so-caued ‘Sinjak-ro’. As conclusions, modem road or ‘Sinjak-ro’ started from modem port to inner hinterland where economic resource or regional center located. A trunk railroad running through Korea Peninsula from Busan to Sinuiju(border between China) is opened its complete operation in 1906 by Japanese investment, when no ‘Sinjak-ro’ road construction begun. Thus from the beginning, railroad station also became important starting point of ‘Sinjak-ro’ as seaports. Before the Japanese annexation of Korea, the ‘Sinjak-ro’ road was constructed mainly between seaport or station, where Japanese commercial settlement located, and hinterlands to help their economic invasion. This study could not deal with other modem transportation systems such as railroads and waterways. It is necessary to examine whole changes of modern transportation systems in this age so that we would comprehend modernization feature of Korea from the viewpoint of transportation history.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1068-1073
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• 2010

When a robot navigates in an outdoor environment, a curb or a sidewalk separated from the road can be used as a robust feature. However, most algorithms could detect the curb only in the straight road, and could not detect highly curved corners, ramps, and so on. This paper proposes an algorithm which enables the robot to detect and follow the curbs in various types of roads. In the proposed method, the robot tilts a laser scanner and computes the error between the predicted and the measured distances to the road in front of the robot. Based on this error, the curbs at corners and curves can be classified. It is also difficult to detect a curb near a ramp because of its low height. In this case, the robot also tilts a laser scanner to detect the curb beyond the ramp. Once the robot classifies the road into the curve, corner, ramp, the robot selects the proper navigation strategies depending on the classified road types and is able to continue to detect and follow the curb. The results of a series of experiments show that the robot can stably detect and follows the curb in curves, corners and ramps as well as the straight road.