• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.423-425
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• 1998

영상검지기를 이용하여 도로상에서 이동중인 차량의 움직임을 탐지하고 분석하는 방법은 지능형교통시스템의 많은 분야에 적용되어질 수 있다. 영상분석으로 움직이는 물체를 탐지하는 방법에는 영상차를 이용하는 방법과 영상차를 이용하지 않는 방법으로 분류할 수 있다. 영상차를 이용하는 방법에서는 영상간의 차영상을 기반으로 하여 물체를 탐지하는 방법은 일반적이고 보편적인 방법이나 시간에 따른 배경영상의 왜곡과 물체의 정체현상에 많은 문제점을 지니고 있다. 그리고 영상차를 이용하지 않는 방법은 영상내의 분석으로 물체를 탐지하는 방법이고, 영상간의 정보를 사용하지 않으므로 영상차에 의한 문제점은 발생되지 않는다. 기존에 연구되어진 영상차를 이용하지 않는 방법은 물체의 형태를 고려하지 않고 단지 이동점의 좌표분석으로 차량의 움직임을 측정하고 있다. 본 논문에서는 영상차를 이용하지 않으며 영상내의 형태정보 분석과 색상정보를 고려하여 기존의 영상검지기가 지니는 문제점을 개선하여 정밀한 차량 추적에 대한 가능성을 알 수 있었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.105-115
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• 2017

Conventional traffic detection sensors such as loop detectors and image sensors are expensive to install and maintain and require different detection algorithms depending on the night and day and have a disadvantage that the detection rate varies widely depending on the weather. On the other hand, the millimeter-wave radar is not affected by bad weather and can obtain constant detection performance regardless of day or night. In addition, there is no need for blocking trafficl for installation and maintenance, and multiple vehicles can be detected at the same time. In this study, a multi-target detection algorithm for a radar sensor with this advantage was devised / implemented by applying a conventional single target detection algorithm. We performed the evaluation and the meaningful results were obtained.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.105-112
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• 1999

Image Processing Technique has been used as an efficient method to collect traffic information on the road such as vehicle counts, speed, queues, congestion and incidents. Most of the current methods which have been used to detect vehicles by the image processing are based on point processing, dealing with the local gray level of each pixel in the small window. However, these methods have some drawbacks. Firstly, detection is restricted by image quality. Secondly, they can not deal with occlusion and perspective projection problems, In this research, a new method which possibly deals with occlusion and perspective problems will be proposed. It extracts spatial information such as the position, the relationship of vehicles in 3-dimensional space, as well as vehicle detection in the image. The main algorithm used in this research is based on an extension of the Hough Transform. The Hough Transform which is proposed to estimates parameters of vertices and directed edges analytically on the Hough Space, is a valuable method for the 3-dimensional analysis of static scenes, motion detection and the estimation of viewing parameters.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.487-491
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• 2008

카메라로부터 얻어지는 화상정보를 처리하여 사람을 검지하는 기술은 많은 분야에 적용될 수 있다. 실제로 많은 어플리케이션에 적용되고 있다. 현재 Tracking 기술에 관련한 다양한 논문과 방법들이 존재한다. 본 논문에서는 스테레오비전이 아닌 2-D조건에서 움직이는 물체와 움직이지 않는 물체를 구분하여, 구분된 영역에서 탬플릿 매칭을 통하여 사람 검지여부를 결정하는 알고리즘을 제안한다.

• Information and Communications Magazine
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• v.32 no.12
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• pp.32-37
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• 2015

본 글에서는 철도교통의 안전성 향상을 위해 철도건널목의 지장물을 검지하는 기술을 소개한다. 철도건널목에 자동차나 보행자 등 열차운행에 지장을 줄 수 있는 물체가 존재하는지 확인하기 위하여 현재 레이저빔 방식의 검지장치를 사용하고 있으나 기술적 한계와 문제점을 보이고 있다. 이를 극복하기 위해 2차원 레이저스캐너 센서를 적용하여 새로운 지장물 검지 시스템을 설계하였으며, 관심구간에 존재하는 물체의 크기와 방향을 검지하는 알고리즘을 탑재하여 효과적인 지장물 인식이 가능하도록 하였다. 제작한 시작품을 실제 운영노선에 설치하여 현장시험을 수행하였다. 본 글에서는 개발된 기술을 설명하고 현장시험 결과를 소개하고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6089-6097
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• 2012

This study suggested a method of detecting an incident inside tunnel by using camera that is installed within the tunnel. As for the proposed incident detection method, a static object, travel except vehicles, smoke, and contra-flow were detected by extracting the moving object through using the real-time background image differencing after receiving image from the camera, which is installed inside the tunnel. To detect the moving object within the tunnel, the positive background image was created by using the moving information of the object. The incident detection method was developed, which is strong in a change of lighting that occurs within the tunnel, and in influence of the external lighting that occurs in the entrance and exit of the tunnel. To examine the efficiency of the suggested method, the experimental images were acquired from Marae tunnel and Expo tunnel in Yeosu of Jeonnam and from Unam tunnel in Imsil of Jeonbuk. Number of images, which were used in experiment, included 20 cases for static object, 20 cases for travel except vehicles, 4 cases for smoke, and 10 cases for contra-flow. As for the detection rate, all of the static object, the travel except vehicles, and the contra-flow were detected in the experimental image. In case of smoke, 3 cases were detected. Thus, excellent performance could be confirmed. The proposed method is now under operation in Marae tunnel and Expo tunnel in Yeosu of Jeonnam and in Unam tunnel in Imsil of Jeonbuk. To examine accurate efficiency, the evaluation of performance is considered to be likely to be needed after acquiring the incident videos, which actually occur within tunnel.

• Journal of IKEEE
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• v.13 no.2
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• pp.232-240
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• 2009

This paper presents a robust vehicle detection and tracking algorithm for supervision of illegal parking. The proposed algorithm is composed of four parts. First, a vehicle detection algorithm is proposed using the improved codebook object detection algorithm to segment moving vehicles from the input sequence. Second, a preprocessing technique using the geometric characteristics of vehicles is employed to exclude non-vehicle objects. Then, the detected vehicles are tracked by an object tracker which incorporates histogram tracking method with Kalman filter. To make the tracking results more accurate, histogram tracking results are used as measurement data for Kalman filter. Finally, Real Stop Counter (RSC) is introduced for trustworthy and accurate performance of the stopped vehicle detection. Experimental results show that the proposed algorithm can track multiple vehicles simultaneously and detect stopped vehicles successfully in the complicated street environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.140-152
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• 2017

This Paper is for A study of sensor fusion using Radar sensor and Vision sensor in moving object detection. Radar sensor has some problems to detect object. When the sensor moves by wind or that kind of thing, it can happen to detect wrong object like building or tress. And vision sensor is very useful for all area. And it is also used so much. but there are some weakness that is influenced easily by the light of the area, shaking of the sensor device, and weather and so on. So in this paper I want to suggest to fuse these sensor to detect object. Each sensor can fill the other's weakness, so this kind of sensor fusion makes object detection much powerful.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.269-278
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• 2018

Railway obstacle detecion system has been introduced with high-speed railway in 2004 to prevent accidents by obstacles such as landslide, rockfall and things fallen from the gauntry over the railway. But existing system has some limitation for landslide or fallen obstacle over railway. Therefore, In this study, we suggest new advanced obstacle detection system introducing the OTDR, optical fiber fences and detection cameras. This system can detect depression degree by the force to the fences and video for the specific region as well as detection wire Off condition. We produce and functional tests for fiber fence and OTDR, which are the core parts of the development system, and results were obtained to demonstrate improved detection capabilities. Several functions also been tested to verify the advanced detection performance and got some satisfactory results. Further we will conduct environment tests and field test.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.