• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 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.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.361-368
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• 2007

In this paper, we propose a smoke detection method which is based on block-wise difference of image frames in video. Our proposed method is composed of three steps which are (a) the detection step of the changed regions against the background, (b) the background update step, and (c) the smoke determination step from the changed regions. We first construct the block mean Image of frames in video. And to extract the changed regions against the background, we use a block-wise difference between background's block mean image and a current input frame's block mean image. After applying projections in block-based difference images, we can determine the changed regions as rectangles using projections of difference images. we propose a update scheme of background's block mean image using the projections. We decide the smoke region using the femoral statistics of the central position and YUV color in the changed region.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.4
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• pp.11-18
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• 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.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.413-416
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• 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.

• Journal of Korean language education
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• v.29 no.4
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• pp.93-112
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• 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.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1044-1051
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• 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.

• Journal of the Korean Institute of Gas
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• v.21 no.5
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• pp.1-8
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• 2017

Currently gas safety management in the industrial field has been done by LDAR as contact method or methane leak detector as non-contact method. But LDAR method requires a lot of man-power and methane leak detector have the limitation of methane only. Therefore the Research on the OGI(optical gas image) has big attention by industry. This research was undertaken to see the effect of background temperature difference of gas cloud on the clarity of OGI. The background temperature control panel was constructed to cool down the background temperature. OGI was taken at the various methane gas ejection rate and the designed temperature difference. The experimental results showed that the OGI(when the temperature difference is $-6^{\circ}C$) is more clear thane the OGI(when the temperature difference is zero). To quantify the clarity difference, MATLAB's RGB analysis method was employed. The RGB value of the OGI at ${\Delta}T-6^{\circ}C$ was 20% lower than the OGI at ${\Delta}T0^{\circ}C$. The clarity difference by T difference can be explained by the total radiation law. When the background temperature of the gas is lower than the air temperature, the radiation energy coming into the OGI lens is increasing. As the energy is increasing, the OGI image becomes clear.

• Journal of Information Technology Applications and Management
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• v.10 no.3
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• pp.45-54
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• 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
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• 2009.02a
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• pp.631-636
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• 2009

Foreground segmentation methods have steadily been researched in the field of computer vision. Especially, background subtraction which extracts a foreground image from the difference between the current frame and a reference image, called as "background image" have been widely used for a variety of real-time applications because of low computation and high-quality. However, if the background scene was dynamically changed, the background subtraction causes lots of errors. In this paper, we propose an efficient background subtraction method in dynamic environment with both static and dynamic scene. The proposed method is a hybrid method that uses the conventional background subtraction for static scene and depth information for dynamic scene. Its validity and efficiency are verified by demonstration in dynamic environment, where a video projector projects various images in the background.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 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.