• Journal of information and communication convergence engineering
• /
• 제13권3호
• /
• pp.197-204
• /
• 2015

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

• Journal of Mechanical Science and Technology
• /
• 제16권2호
• /
• pp.137-146
• /
• 2002

In this paper, a path generation algorithm that uses sensor scannings is described. A scanning algorithm for recognizing the ambient environment of the Automatic Guided Vehicle (AGV) that uses the information from the sensor platform is proposed. An algorithm for computing the real path and obstacle length is developed by using a scanning method that controls rotating of the sensors on the platform. The AGV can recognize the given path by adopting this algorithm. As the AGV with two-wheel drive constitute a nonholonomic system, a linearized kinematic model is applied to the AGV motor control. An optimal controller is designed for tracking the reference path which is generated by recognizing the path pattern. Based on experimental results, the proposed algorithm that uses scanning with a sensor platform employing only a small number of sensors and a low cost controller for the AGV is shown to be adequate for path generation.

• 한국자동차공학회논문집
• /
• 제22권3호
• /
• pp.50-59
• /
• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.

• 한국ITS학회 논문지
• /
• 제8권6호
• /
• pp.187-195
• /
• 2009

최근 유비쿼터스 컴퓨팅의 발전과 함께 유비쿼터스 환경에 적용할 수 있는 상황인식 모델에 대한 연구 개발이 요구되고 있다. 본 논문은 자동차 공간을 위한 매트릭스 기반 상황인식 모델을 설계하고 구현하였으며, 상황인식 모델링을 위해 5W1H와 CAM 수식을 이용한 매트릭스 구성 방법을 제안하였다. 개인 식별과 위치 확인을 위한 Zigbee 모듈과 GPS의 현재의 공간과 시간 정보를 위한 네비게이터를 이용하여 제안된 모델을 이용한 시스템을 구현하였다. 시험 결과 제안된 모델이 유용 가능함을 보였다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.110-113
• /
• 2003

Most traffic accidents are caused by drivers' carelessness and lack of information on the surrounding objects. In this paper we proposed a model of human intention recognition through case-base learning and to build up an experiment system. The system can help us recognize object's intention (e.g. turn left, turn right or straight) by using detected data about human's motion, speed of the car and the distance between the car and the intersection. Furthermore, we included an example using case-base learning in this paper to improve the precision of recognition as well as an example to explain the use of the system. PC can be used to predict the driving reaction beforehand and send a warning signal to the driver in time if there is any danger.

• 한국항공우주학회지
• /
• 제49권1호
• /
• pp.63-73
• /
• 2021

본 논문은 공중 혹은 해상배경에 표적과 화염이 동시에 존재할 때, 무인항공기에 장착된 EOTS(Electro-Optical Targeting System; 전자광학 추적장비)가 표적을 추적하기 위해 화염의 영향에 강건하도록 표적을 자동 인식하는 기법을 제안한다. 제안한 기법은 표적과 화염의 적외선 영상을 Gradient Vector Field로 변환하고, 각 Gradient magnitude를 Polynomial Curve Fitting 도구에 적용하여 다항식 계수를 추출 및 얕은 신경망 모델에 학습함으로써, 표적과 화염을 자동으로 인식한다. 확보한 표적 및 화염의 다양한 적외선 영상 DB를 학습데이터, 검증데이터, 시험데이터로 분류하여 제안한 기법의 표적 및 화염 자동 인식 성능을 확인하였다. 본 알고리듬을 활용하여 무인항공기의 자동비행 중 충돌회피, 산불탐지, 공중 및 해상의 목표물을 자동탐지 및 인식하는 분야에 적용될 수 있다.

• International Journal of Aeronautical and Space Sciences
• /
• 제18권3호
• /
• pp.414-424
• /
• 2017

This paper introduces an experimental device which can measure accurate aerodynamic forces without support interference in wide experimental region for wind tunnel test of micro aerial vehicles (MAVs). A stereo pattern recognition (SPR) method was introduced to a magnetic suspension and balance system (MSBS), which can eliminate support interference by levitating the experimental model, to establish wider experimental region; thereby MSBS-SPR integrated system was developed. The SPR method is non-contact, highly accurate three-dimensional position measurement method providing wide measurement range. To evaluate the system performance, a series of performance evaluations including SPR system measurement accuracy and 6 degrees of freedom (DOFs) position/attitude control of the MAV model were conducted. This newly developed system could control the MAV model rapidly and accurately within almost 60mm for translational DOFs and 40deg for rotational DOFs inside of $300{\times}300mm$ test section. In addition, a static wind tunnel test was conducted to verify the aerodynamic force measurement capability. It turned out that this system could accurately measure the aerodynamic forces in low Reynolds number, even for the weak forces which were hard to measure using typical balance system, without making any mechanical contact with the MAV model.

• ETRI Journal
• /
• 제37권3호
• /
• pp.606-616
• /
• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

• Smart Structures and Systems
• /
• 제24권5호
• /
• pp.617-630
• /
• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• 대한교통학회지
• /
• 제22권2호
• /
• pp.103-108
• /
• 2004

본 연구는 신호교차로의 정지선 통과차량의 차두시간 결정은 단순히 출발순서에 영향을 받아 결정되지만은 않는다는 문제의식에서 출발하였다. 실제 신호교차로의 개별차량 통과시 속도와 차두시간 자료를 검지기와 검지알고리즘을 활용 파악하고 분석에 활용하였다. 단순 자료분석에서는 차량의 통행행태를 결정하는 모형을 정립하는 것이 무의미한 것으로 파악될 수 있는 통계분석 결과가 나타났다. 그러나, 공격적인 운전행태를 가진 운전자가 운전하는 차량의 자료를 선별하고 자료 스케일 조정(ln값 활용)을 통해 결정계수 0.91수준의 모형이 설정되었다. 구축된 모형은 교차로 정지선에서 통과하는 차량의 차두시간은 앞차의 속도, 차두시간과 자체차량의 속도에 영향을 받는 사실을 밝혀주었다.