• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.258-261
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• 2015

본 논문에서는 주행 중에 보행자의 인식 및 추적을 위해서 차량에서 촬영된 영상정보를 이용하여 주변의 보행자를 찾고, 사고 위험성이 있는 보행자를 인식하기 위해서 보행자 파악 및 보행자와의 거리를 측정하기 위한 연구를 하고자 한다. 본 논문에서는 차량에 정착된 카메라를 통한 보행자 인식 기술에 대해 연구 하였다. 제안한 방법은 보행자 인식 단계에서 Cascasde HOG, Haar-like 알고리즘을 적용하였고, 추적 단계에서 칼만 필터와 클러스터링 기법을 결합하여 실시간으로 보행자를 인식 및 추적하였다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.359-362
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• 2001

본 논문에서는 광학센서와 적외선 센서를 사용하는 Multi-sensor 시스템에서 영상 정보를 통한 물체의 추적 및 인식에 필요한 영상을 분리하는데 필요한 전처리와 object 기반의 추적 방법을 제안하였다. 일반적인 추적 알고리즘의 목표는 consistency를 유지하는데 있다. 그러나 인식에 필요한 영상을 분리하기 위해서는 물체의 범위를 정확히 판단 할 수 있는 능력이 중요하다. 이를 위해 CCD와 IR영상에 동시에 적용 가능한 전처리 기법과 object 기반의 two-step 추적 알고리즘을 통해 consistency외에도, 물체의 범위를 estimation하여 인식에 필요한 범위를 분리해 낸다. 본 논문에서는 ITS 의 ETCS application을 위해 이종 센서인 CCD와 IR의 야간 차량 영상정보를 이용하여 알고리즘을 test 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.254-257
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• 2015

차량 인식 기술은 운전자에게 차량 충돌과 같은 위험요소를 사전에 인식시키거나 차량을 자동으로 제어하는 기술로 각광 받고 있다. 본 논문에서는 입력 영상에서 차량이 나타날 수 있는 관심 영역을 설정한 다음 미리 학습된 검출기를 통한 Haar-like와 Adaboost 알고리즘으로 차량 후보 영역을 검출하고 중복된 영역을 제거하기 위인식 기술해 클러스터링 기법을 적용하고, 칼만필터로 프레임 영상에서 차량을 추적 하고, 다시 중복된 영역에 대해 클러스터링 기법을 적용하는 방법을 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.213-215
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• 2011

USN(Ubiquitous Sensor Network) 환경은 언제, 어디서나 정보를 이용할 수 있게 한다. 이러한 유비쿼터스 환경은 자유로운 커뮤니케이션을 보다 쉽게 제공할 수 있으며, 특히 u-교통 환경을 구축하는데 기본적인 요소기술이 된다. 또한 u-교통 환경을 구축하기 위해서는 차량의 위치를 보다 정확히 인식할 수 있는 기술이 요구된다. 위치측정은 센서노드의 전파를 이용하거나 거리를 측정할 수 있는 센서를 활용하여 목표물의 위치를 계산한다. 본 논문은 센서노드의 RSSI(Received Signal Strength Indication) 신호 값을 분류하고 주변노드의 위치와 통신범위 및 세기정보를 최대한 활용하여 이동차량을 보다 정확하게 추적할 수 있는 위치추적기법을 제시한다. 일부 시뮬레이션 결과, 제안된 기법이 기존 위치추적 알고리즘 보다 u-교통 환경에서 위치추적의 정확도가 우수함을 증명하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.115-125
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• 2023

This paper relates to the development of CCTV camera posture calibration algorithm that can simultaneously collect traffic information such as traffic volume and speed in the state of view of the CCTV camera set for traffic monitoring. The developed autonomous posture calibration algorithm uses vehicle recognition and tracking techniques to identify the road, and automatically determines the angle of view for the operator's traffic surveillance and traffic information collection. To verify the performance of the proposed algorithm, a CCTV installed on site was used, and the results of the angle of view automatically calculated by the autonomous posture calibration algorithm for the angle of view set for traffic surveillance and traffic information collection were compared.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.689-698
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• 2021

In this paper, lane recognition, steering control and speed control algorithms were developed using Bluetooth wireless communication and image processing techniques. Instead of recognizing road traffic signals based on image processing techniques, a methodology for recognizing the permissible road speed by receiving speed codes from electronic traffic signals using Bluetooth wireless communication was developed. In addition, a steering control algorithm based on PWM control that tracks the lanes using the Canny algorithm and Hough transform was developed. A vehicle prototype and a driving test track were developed to prove the accuracy of the developed algorithm. Raspberry Pi and Arduino were applied as main control devices for steering control and speed control, respectively. Also, Python and OpenCV were used as implementation languages. The effectiveness of the proposed methodology was confirmed by demonstrating effectiveness in the lane tracking and driving control evaluation experiments using a vehicle prototypes and a test track.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.235-240
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• 2015

Active Safety system is requested on the market of the medium and heavy duty commercial vehicle over 4.5ton beside the market of passenger car with advancement of the digital equipment proportionally. Unlike the passenger car, the mounting position of camera in case of the medium and heavy duty commercial vehicle is relatively high, it is disadvantaged conditions for lane recognition in contradiction to passenger car. In this work, we show the method of lane recognition through the Sobel edge, based on the spatial domain processing, Hough transform and color conversion correction. Also we suggest the low error method of front vehicles recognition in order to reduce the detection error through Haar-like, Adaboost, SVM and Template matching, etc., which are the object recognition methods by frontal camera vision. It is verified that the reliability over 98% on lane recognition is obtained through the vehicle test.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.475-478
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• 2014

This paper proposed the position tracking and recognition method of workers and cars in construction field. The reason why the position of workers and cars has to be tracked is to prevent the safety accident, emergency rescue, and theft of building materials by unauthorized people and car, etc. To realize the tracking, it needs to adopt the Information Telecommunication technology to the construction field as an integrated support system. In this paper, we proposed the continuous positioning tracking algorithm for workers and moving cars using the selected wireless communication network. And we proposed the virtual gateway method to detect the entrance status of moving workers and cars. All of these proposed methods are evaluated in real construction field using the prototype of support system made by ourselves.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2277-2282
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• 2009

In this paper, we proposed a new recognition method can be used in application systems using morphological features, difference operators and ART2 algorithm. At first, edges are extracted from an acquired car image by a camera using difference operators and the image of extracted edges is binarized by a block binarization method. In order to extract license plate area, noise areas are eliminated by applying morphological features of new and existing types of license plate to the 8-directional edge tracking algorithm in the binarized image. After the extraction of license plate area, mean binarization and mini-max binarization methods are applied to the extracted license plate area in order to eliminated noises by morphological features of individual elements in the license plate area, and then each character is extracted and combined by Labeling algorithm. The extracted and combined characters(letter and number symbols) are recognized after the learning by ART2 algorithm. In order to evaluate the extraction and recognition performances of the proposed method, 200 vehicle license plate images (100 for green type and 100 for white type) are used for experiment, and the experimental results show the proposed method is effective.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.