• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.63-73
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• 2003

COSMOS(Cycle, Offset, Split Model for Seoul), a real-time traffic adaptive signal system. estimates queue lengths on each approach on the basis of arithmetic average spot speeds calculated on loop detectors installed at each of two adjacent lanes. In this paper, A new method, a traffic volume-weighted average method, was studied and compared with the existing arithmetic average method. It was found that the relationship between the ratio of volumes of two lanes and the difference of average speed of each lane has a linear form. With field data, The two methods were applied and the proposed method shows more stable and reasonable queue estimation results.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.13-20
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• 2007

For calculating the number of lane, it is essential to gain the 30th or 100th highest design hourly volume. The design hourly volume obtained from AADT multiplied by design hour factor. In this paper, we developed the regression models fur estimating the 30th highest hour volume and 100th highest hour volume as defined by AADT 50,000 criterion based on the data obtained the 34 monitoring sites in highway. By comparing the performance of the proposed models and conventional models using MAPE, the proposed model for 30th highest design hourly volume reduced the estimator error of 11.83% than that of conventional methods for less than AADT 50,000 and decreased estimation error of 22.17% than that of conventional method for more than AADT 50,000. Moreover, the proposed model for 100th highest design hourly volume reduced the estimator error of 8.16% than that of conventional methods for less than AADT 50,000 and decreased estimation error of 15.25% than that of conventional method for more than AADT 50,000.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1697-1703
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• 2020

Sensor nodes that directly collect data from the surrounding environment have many constraints, such as power supply and memory size, thus efficient use of resources is required. In this paper, in a sensor node that receives location data of a vehicle on a lane, the data reception period is changed by the target's speed estimated by the Kalman filter and distance weight. For a slower speed of the vehicle, the longer data reception interval of the sensor node can reduce the processing time performed in the entire sensor network. The proposed method was verified through a traffic simulator implemented as MATLAB, and the results achieved that the processing time was reduced in the entire sensor network using the proposed method compared to the baseline method that receives all data from the vehicle.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1536-1543
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• 2011

Video stabilization is a very important step for vision based applications in the vehicular technology because the accuracy of these applications such as obstacle distance estimation, lane detection and tracking can be affected by bumpy roads and oscillation of vehicle. Conventional methods suffer from either the zooming effect which caused by a camera movement or some motion of surrounding vehicles. In order to overcome this problem, we propose a novel video stabilization method using FOE(Focus of Expansion). When a vehicle moves, optical flow diffuses from the FOE and the FOE is equal to an epipole. If a vehicle moves with vibration, the position of the epipole in the two consecutive frames is changed by oscillation of the vehicle. Therefore, we carry out video stabilization using motion vector estimated from the amount of change of the epipoles. Experiment results show that the proposed method is more efficient than conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.42-51
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection, especially active shadows resulted from moving vehicles. In this paper, a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98% in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic parameters concerning traffic flow is obtained to describe the load of each lane.

• Journal of IKEEE
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• v.24 no.2
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• pp.492-496
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• 2020

The visibility deteriorates in hazy weather and it is difficult to accurately recognize information captured by the camera. Research is being actively conducted to remove haze so that camera-based applications such as object localization/detection and lane recognition can operate normally even in hazy weather. In this paper, we propose a nonlinear model for depth map estimation through an extensive analysis that the difference between brightness and saturation in hazy image increases non-linearly with the depth of the image. The quantitative evaluation(MSE, SSIM, TMQI) shows that the proposed haze removal method based on the nonlinear model is superior to other state-of-the-art methods.

• Journal of Environmental Science International
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• v.22 no.5
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• pp.563-570
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• 2013

Roughness coefficient was computed for review of applicability based on measurement of the representative grain diameter reflecting channel characteristics of Han Stream. After field survey, collection of bed material, and grain analysis on the collected bed material, roughness coefficient was computed using representative grain and existing empirical equation for roughness coefficient. Value of roughness coefficient calculated using equation by Meyer-Peter and Muller (1948) was 0.0417 for upstream, 0.0432 for midstream, and 0.0493 for downstream. As a result of comparing the computed roughness coefficient to other empirical equations for review of applicability, the coefficient was larger in Strickler (1923) equation by 0.006. Smaller coefficient was shown by Planning Report for River Improvement Works. Equation by Garde and Raju (1978) was larger by 0.004, and equations by Lane and Carlson (1953) and by Meyer-Peter and Muller (1948) were larger by 0.001. Such precise roughness coefficient is extremely important when computing the amount of flood in rivers to prevent destruction of downstream embankments and property damages from flooding. Since roughness coefficient is a factor determined by complicated elements and differs according to time and space, continued management of roughness coefficient in rivers and streams is deemed necessary.