• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.86-96
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• 2010

In this paper, we propose stereo vision-based obstacle detection and vehicle verification methods using U-disparity map and bird's-eye view mapping. First, we extract a road feature using maximum frequent values in each row and column. And we extract obstacle areas on the road using the extracted road feature. To extract obstacle areas exactly we utilize U-disparity map. We can extract obstacle areas exactly on the U-disparity map using threshold value which consists of disparity value and camera parameter. But there are still multiple obstacles in the extracted obstacle areas. Thus, we perform another processing, namely segmentation. We convert the extracted obstacle areas into a bird's-eye view using camera modeling and parameters. We can segment obstacle areas on the bird's-eye view robustly because obstacles are represented on it according to ranges. Finally, we verify the obstacles whether those are vehicles or not using various vehicle features, namely road contacting, constant horizontal length, aspect ratio and texture information. We conduct experiments to prove the performance of our proposed algorithms in real traffic situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.43-53
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• 2010

The increasing interest in the use of automated photographic equipment in Korea enables to detect speed violation and enforce traffic laws. Since 1996, Korean National Police Agency has been promoting a project for installation of Automated Speed Enforcement (ASE) system aiming at reduction of accidents. Recently, point to point speed cameras operate to measure a driver's average speed across length of road and/or the spot speed at individual camera sites along the road length. In this study, we were analyzed to driver's operating characteristics in point to point speed cameras. The point to point speed camera system has link-long influence on drivers and their speeds despite enforcement being visible only at the start and end of enforced road length. Then, mean and 85th percentile speeds were below speed limit along enforced road length by the two camers. Also, Female drivers were driving at low speed than male drivers along the road length.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.41-47
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• 2019

In this study, a smart assistive device is designed to recognize pedestrian signal and to provide audio instructions for visually impaired people in crossing streets safely. Walking alone is one of the biggest challenges to the visually impaired and it deteriorates their life quality. The proposed device has a camera attached on a pair of glasses which can detect traffic lights, recognize pedestrian signals in real-time using a machine learning algorithm on GPU board and provide audio instructions to the user. For the portability, the dimension of the device is designed to be compact and light but with sufficient battery life. The embedded processor of device is wired to the small camera which is attached on a pair of glasses. Also, on inner part of the leg of the glasses, a bone-conduction speaker is installed which can give audio instructions without blocking external sounds for safety reason. The performance of the proposed device was validated with experiments and it showed 87.0% recall and 100% precision for detecting pedestrian green light, and 94.4% recall and 97.1% precision for detecting pedestrian red light.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.214-223
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• 2024

The recent increase in maritime traffic and coastal leisure activities has led to a rise in various marine accidents. These incidents not only result in damage to human life and property but also pose a significant risk of marine pollution involving oil and hazardous and noxious substances (HNS) spills. Therefore, effective ship monitoring is crucial for preparing and for responding to marine accidents. This study conducted an aerial experiment utilizing hyperspectral remote sensing to develop a maritime ship monitoring system. Hyperspectral aerial measurements were carried out around Gungpyeong Port in the western coastal region of the Korean Peninsula, and spectral libraries were constructed for various ship decks. The spectral correlation similarity (SCS) technique was employed for ship detection, analyzing the spatial similarity distribution between hyperspectral images and ship spectra. As a result, 15 ships were detected in the hyperspectral images. The color of each ship's deck was classified based on the highest spectral similarity. The detected ships were verified by matching them with high-resolution digital mapping camera (DMC) images. This foundational study on the application of aerial hyperspectral sensors for maritime ship detection demonstrates their potential role in future remote sensing-based ship monitoring systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.35-45
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• 2008

Realtime visibility measurement using camera is a new method which can collect some realtime visibility data similar to those of the human eyes, and can replace the existing methods using the expensive optical equipment. There have been a few attempts to measure visibility by extracting depth and three-dimensional structure under bad weather conditions. However, if there are many movements of objects in the image, these approaches seem to be inappropriate. In addition, the realtime visibility measurement will require a relatively simple and fast processing. Typically the contrast degrades exponentially in the bad weather. Therefore, in this paper we propose an easy and quick method that extract contrast from images using a moving area filter and measure visibility by mathematically modelling of the relationship between image contrast and visibility. The moving area filter is used for removing the area of the sky and moving objects that affect visibility measurement on images. The method proposed here can make possible not only realtime visibility measurement from images taken by CCD cameras, but also steady visibility measurement by using the moving area filter in case of much traffic on the road.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.125-138
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• 2011

The highway visibility reduction caused by fog is one of the major elements of traffic accidents. Though the fog warning systems can lead drivers into safe driving by letting them aware dangerous situations in advance, the optical sensors, such as fog sensor, has been extremely costly. Through recent studies, it is delivered that visibility measurements have become obtainable with relatively cheap cameras and their functionality is as similar as a driver' visual sense. Those measurements however require additional signs or ROI, so it is still costly and unable to utilize the conventional images from the existing systems. This study proposes a new method to detect the visibility in real time based on the conventional images from the existing CCTV cameras. The proposed method builds a road model and extracts and applies vehicle movements and visible lines - those highlight easy and quick visibility measurements. The proposed method has advantages of both (1) having possible day and night visibility measurements similar to drivers' visual sense and (2) being easily applied to the existing CCTV system without additional devices. This paper presents field experiments using images acquired from the Central Inland Expressway and discusses future research directions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.7-14
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• 2012

We frequently confront with cars interfering into our lane on the ramp. We suffered from serious traffic congestion due to the interfering cars. But the police enforcement has not done actively because it's hard to enforce. In this study, we have evaluated the systems to enforce cutting-in cars through the field test. Generally, the image processing method depends on the weather. To overcome this limitation we proposed a new algorithm combined with section detection method. In the filed test we concluded the results as follows. Whereas the violation detection rate of the general image processing was 58.2%, a new algorithm proposed by this study was 74.5%. And, an error rate enforcing vehicles that do not violate was 0.0%. Also, we can use the existing facilities, such as street light because of compact and lightweight systems which are integrated camera with controller. Therefore, we concluded that it is possible to enforce the interfering Cars using vehicle enforcement systems.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.69-76
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• 2016

OBJECTIVES : The objective of this study is to propose a capacity analysis methodology for riverside bike-exclusive roads. METHODS : Three steps were performed to develop a methodology to estimate bikeway capacity. First, we reviewed previous studies on the vehicle-road capacity analysis and proposed their applicability to bikeways. Second, two assumptions were made based on the traffic flow characteristics of bikeways: (1) the capacitated state in bikeways occur within a bicycle platoon, and (2) a bicycle platoon consists of more than three bicycles running in close proximity. In addition, it is assumed that the mean time headway of a bicycle platoon represents the characteristics of the platoon. The normality of the mean-time headway of a bicycle platoon calculated using the central limit theorem leads to the development of a method that estimates the riverside bikeway capacity using data collected from two different riverside bike-exclusive roads (Han-river and Anyangcheon). We used a location-fixed video camera to record videos of running bicycles and wrote a special-purpose software program to code the time-headway data from the videos. RESULTS : Time headways from 189 bicycle platoons were analyzed. The estimated mean-time headway of the capacitated bicycle flow is 1.01 s, from which the capacity of the bikeway is found to be 3578 vehicles/h. CONCLUSIONS : The proposed method that estimates bikeway capacity could be applicable to the analysis of short-range congested area rather than planning the number of lanes. In other words, it determines the sections that are temporarily highly congested and proposes appropriate strategies to mitigate the congestion.