• Journal of Information Processing Systems
• /
• 제15권5호
• /
• pp.1218-1230
• /
• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• 정보처리학회논문지B
• /
• 제14B권5호
• /
• pp.361-368
• /
• 2007

본 논문은 비디오 영상에서 블록기반 차영상을 이용한 연기검출 방법을 제시한다. 제안된 방법은 배경으로부터 변경된 영역 검출 단계, 배경영상 갱신단계, 검출된 영역이 연기인지를 판단하는 단계의 세 단계로 구성된다. 입력 비디오에서 각 프레임의 블록 평균영상을 계산하였으며, 변화영역을 검출하기 위하여 배경영상의 블록평균영상과 입력영상의 블록평균영상의 차이를 사용한다. 블록기반 차영상을 투영하여 변화된 사각영역을 검출한다. 차영상의 투영을 이용한 배경블록평균영상의 갱신방법을 제안한다. 변화영역의 중심위치 및 YUV 색상의 시간적 특징을 이용하여 연기영역을 판단한다.

• 조명전기설비학회논문지
• /
• 제21권4호
• /
• pp.11-18
• /
• 2007

A moving object detection algorithm for surveillance video is here proposed which employs background initialization based on two-stage median filtering and a background updating method based on state transition diagram. In the background initialization, the spatiotemporal similarity is measured in the subinterval. From the accumulated difference between the base frame and the other frames in a subinterval, the regions affected by moving objects are located. The median is applied over the subsequence in the subinterval in which regions share similarity. The outputs from each subinterval are filtered by a two-stage median filter. The background of every frame is updated by the suggested state transition diagram The object is detected by the difference between the current frame and the updated background. The proposed method showed good results even for busy, crowded sequences which included moving objects from the first frame.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
• /
• pp.413-416
• /
• 2003

This paper proposes an adaptive background image generation method based on the frame difference for traffic monitoring. The performance of the conventional method is limited when there are more vehicles due to traffic Jam. To improve on this, we use frame differencing to separate vehicles from background in frame differencing, we adopt selective approach by using part of the image not considered as vehicle fer extraction of background. The proposed method generates background more efficiently than conventional methods even in the presence of heavy traffic.

• 한국어교육
• /
• 제29권4호
• /
• pp.93-112
• /
• 2018

The purpose of this study is to deal with vocabulary in KFL. As a result of this study, learners learned vocabulary on average 43 points through contextual inference and introduction of the class to activate background knowledge. In particular, the implicit method showed the highest learning rate of 52 points, and the thematic method had a 41 point-learning rate. In contrast, the semantic method was the lowest with a 25 point-learning rate. There was no significant difference in the improvement rate of upper vocabulary learners, but in the case of the lower learner, there was significant difference in the improvement rate. The difference was not significant in the post-test relative gain rate of upper learners, but there was significant in lower learners. In the delayed test relative gain rate, the difference was significant in all groups. There was correlation between vocabulary difficulty and score, but there was no correlation with the thematic method. And there was no correlation between vocabulary difficulty, improvement rate and relative gain rate in all three classes. However, content understanding, lexical grade, improvement rate, and relative gain rate showed a significant correlation.

• 한국멀티미디어학회논문지
• /
• 제21권9호
• /
• pp.1044-1051
• /
• 2018

In this paper, we propose a new method to improve low light image. In order to improve the image quality of a night image with a moving object as much as the quality of a daytime image, the following tasks were performed. Firstly, we reduce the noisy of the input night image and improve the night image by the tone mapping method. Secondly, we segment the input night image into a foreground with motion and a background without motion. The motion is detected using both the difference between the current frame and the previous frame and the difference between the current frame and the night background image. The background region of the night image takes pixels from corresponding positions in the daytime image. The foreground regions of the night image take the pixels from the corresponding positions of the image which is improved by the tone mapping method. Experimental results show that the proposed method can improve the visual quality more clearly than the existing methods.

• 한국가스학회지
• /
• 제21권5호
• /
• pp.1-8
• /
• 2017

현재 산업현장의 가스 안전관리는 접촉식은 LDAR(Leak Detection and Repair), 비접촉식은 레이저 메탄검지기와 IR 카메라를 사용하고 있다. LDAR 방식은 전체 관리를 하는데 많은 인력과 소요시간이 들고, 관리자가 측정을 위해 가까이 접근해야 하므로 안전의 위협을 받을 수 있어 비접촉식이 더 효율적이다. 비접촉식에서 IR(infrared)을 이용한 가스 측정 방안에 대한 연구가 주목받고 있다. 산업 가스 중 메탄가스를 활용하여 측정 거리에 따라 가스 분출량을 변화시켜 OGI(optical gas image)를 촬영하였다. 본 논문은 가스의 배경온도차이가 OGI 의 선명도에 미치는 영향을 확인하기 위한 실험이다. OGI를 통해 가스의 구름모형을 정확하고, 선명하게 보기 위하여 배경온도 조절판을 제작하였다. 배경온도 조절판을 통해 배경온도와 대기온도 차이가 ${\Delta}T0^{\circ}C$일 때 보다 ${\Delta}T-6^{\circ}C$ 차이의 낮은 온도 조건으로 OGI 촬영을 한 결과가 육안을 확인하였을 때 더 선명한 차이가 나타났다. 선명도 차이의 객관성을 부여하기 위하여 추가로 MATLAB 의 RGB 분석법으로 확인한 결과, ${\Delta}T$가 $-6^{\circ}C$ 일 경우 RGB 값의 수치가 약 20% 낮게 나왔다. 배경온도가 대기온도보다 $-6^{\circ}C$ 낮을 때 더 선명하게 보이는 것은 총 복사법칙으로 설명이 가능하다. 가스의 배경온도가 대기온도에 비해 낮게 될 때 OGI 렌즈로 들어오는 가스의 복사에너지가 증가되어 가스가 더 선명하게 보이게 된다.

• Journal of Information Technology Applications and Management
• /
• 제10권3호
• /
• pp.45-54
• /
• 2003

Security system with web camera remarkably has been developed at an Internet era. Using transmitted images from remote camera, the system can recognize current situation and take a proper action through web. Existing motion detection methods use simply difference image, background image techniques or block matching algorithm which establish initial block by set search area and find similar block. But these methods are difficult to detect exact motion because of useless noise. In this paper, the proposed method is updating changed background image as much as $N{\times}M$pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect motion by computing fixed distance pixel instead of operate all pixel.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2009년도 학술대회
• /
• pp.631-636
• /
• 2009

컴퓨터 비전 분야에서 전경을 추출하기 위한 영역 분할(segmentation) 방법에 대한 연구가 활발히 진행되어 왔다. 특히, 전경이 배제된 배경 영상과 현재 프레임의 차이를 이용하여 전경을 추출하는 배경 차분(background subtraction) 방법은 요구하는 계산량에 비해 우수한 품질의 전경 추출이 가능하므로 실시간 처리가 필요한 비전 시스템에 다양하게 응용되고 있다. 그러나 배경 차분 방법만을 이용하여서는 배경이 동적으로 변하는 환경에서 정확한 전경을 추출해 내지 못하는 단점이 있다. 본 논문에서는 정적인 배경과 동적인 배경이 공존하는 환경에서 영역 분할을 효과적으로 수행하는 방법을 제안한다. 제안된 방법은 정적인 배경 영역에 대해서는 기존의 배경 차분 방법을 이용하여 전경을 추출하고, 동적인 배경 영역에 대해서는 깊이 정보를 이용하여 전경을 추출하는 하이브리드 방식을 사용한다. 정적인 배경에 동적인 영상을 프로젝터로 투영하는 환경에서 제안된 방법의 효율성을 검증하였다.

• 제어로봇시스템학회논문지
• /
• 제16권1호
• /
• pp.69-76
• /
• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.