• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.281-284
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• 2003

In this study, we have proposed the method of moving area detection as the preprocessing step of moving object tracking system. First, we catch the two frames which are different at time in image sequence. We obtain the moving area by using their binary differential image. In differential image, the object area of previous and current frame is present. In the tracking system, the background is changed by camera motion. So, in this case we have to decide which moving area of object is current at time. We obtain the binary edge image of current frame by applying a threshold to the output of an edge detector. Then we performed logical AND operation between the edge image and differential image. As a result of this work moving area of object can be detected.

• Korean Institute of Interior Design Journal
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• v.25 no.2
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• pp.21-29
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• 2016

This study has estimated the traits of conscious eye moving with the objects of the halls of subway stations. For that estimation, the observation data from eye-tracking were matched with the experiment images, while an independent program was produced and utilized for the analysis of the eye moving in the selected sections, which could provide the ground for clarifying the traits of space-users' eye moving. The outcomes can be defines as the followings. First, The application of the independently produced program provides the method for coding the great amount of observation data, which cut down a lot of analysis time for finding out the traits of conscious eye moving. Accordingly, the inclusion of eye's intentionality in the method for extracting the characteristics of eye moving enabled the features of entrance and exit of particular objects with the course of observing time to be organized. Second, The examination of eye moving at each area surrounding the object factors showed that [out]${\rightarrow}$[in], which the line of sight is from the surround area to the objects, characteristically moved from the left-top (Area I) of the selected object to the object while [in]${\rightarrow}$[out], which is from the inside of the object to the outside, also moved to the left-top (Area I). Overall, there were much eye moving from the tops of right and left (Area I, II) to the object, but the eye moving to the outside was found to move to the left-top (Area I), the right-middle (Area IV) and the right-top (Area II). Third, In order to find if there was any intense eye-moving toward a particular factor, the dominant standards were presented for analysis, which showed that there was much eye-moving from the tops (Area I, II) to the sections of 1 and 2. While the eye-moving of [in] was [I $I{\rightarrow}A$](23.0%), [$I{\rightarrow}B$](16.1%) and [$II{\rightarrow}B$](13.8%), that of [out] was [$A{\rightarrow}I$](14.8%), [$B{\rightarrow}I$](13.6%), [$A{\rightarrow}II$](11.4%), [$B{\rightarrow}IV$](11.4%) and [$B{\rightarrow}II$](10.2%). Though the eye-moving toward objects took place in specific directions (areas), that (out) from the objects to the outside was found to be dispersed widely to different areas.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1240-1245
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• 2004

This paper presents the moving objects segmentation algorithms from the sequence images in the stationary backgrounds such as surveillance camera and video phone and so on. In this paper, the moving object area is extracted with proposed object searching algorithm and then moving object is segmented within the moving object area. Also the proposed algorithms have the robustness against noise problems and results show the proposed algorithm is able to efficiently segment and track the moving object area.

• Journal of Families and Better Life
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• v.22 no.6 s.72
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• pp.1-11
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• 2004

The purpose of this study was to provide basic information about efficient space use in the dining and bathing area through the analysis of service flowing. Four researchers observed the service flowing and the using behavior at those areas. The results of the study were as follows: Dining service was proceeded as resident moving, waiting, meal serving, dining, moving and arranging in order. The waiting stage was one of the problematic processes since the staffs made the residents wait to) long at a fixed position. The program right before the meal serving will be helpful for reducing tediousness of the elderly residents. Another problem was that the area was not big enough for the meal sowing. The legal regulation Is needed to prescnbe for the size of dining area per resident. The flowing of bathing service was proceeded as staff preparation, moving, waiting, undressing, bathing, drying, dressing, moving and arranging in order. There were more problems in the dressing area than in the bathing area. The elderly with stretcher or wheelchairs had difficulty in entering the narrow doorway. The dressing area was so crowded with the staffs, undressed elderly, dressed elderly, and other laundries. The division of dressing and undressing area is required to avoid the confusion of the users in the area.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.823-826
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• 2004

In this paper, A tracking of the moving objects using normalized hue distribution in HSI color model was proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area. Hue information of the detected moving area are normalized by 24 levels from $0^{\circ}$ to $3600^{\circ}A$ distance in between normalized levels with a hue distribution chart of the normalized moving objects is used for the identity distinction feature parameters of the moving objects. To examine proposed method in this paper, image of moving cars are obtained by setting up three cameras at different places every 1 km on outer motorway. The simulation results of identity distinction show that it is possible to distinct the identity a distance in between normalization levels of a hue distribution chart without background.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.591-594
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• 2004

In this paper, The identity distinction of the moving objects using distance among hue normalization levels was proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area. Hue information of the detected moving area are normalized by 24 levels from 0$^{\circ}$ to 360$^{\circ}$. A distance in between normalized levels with a hue distribution chart of the normalized moving objects is used for the identity distinction feature parameters of the moving objects. To examine proposed method in this paper, image of moving cars are obtained by setting up three cameras at different places every 1 km on outer motorway. The simulation results of identity distinction show that it is possible to distinct the identity a distance in between normalization levels of a hue distribution chart without background.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.321-329
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• 1999

Moving Object segmentation extracts an interested moving object on a consecutive image frames, and has been used for factory automation, autonomous navigation, video surveillance, and VOP(Video Object Plane) detection in a MPEG-4 method. This paper proposes new segmentation method using difference images are calculated with three consecutive input image frames, and used to calculate both coarse object area(AI) and it's movement area(OI). An AI is extracted by removing background using background area projection(BAP). Missing parts in the AI is recovered with help of the OI. Boundary information of the OI confines missing parts of the object and gives inital curves for active contour optimization. The optimized contours in addition to the AI make the boundaries of the moving object. Experimental results of a fast moving object on a complex background scene are included.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.35-45
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• 2008

Realtime visibility measurement using camera is a new method which can collect some realtime visibility data similar to those of the human eyes, and can replace the existing methods using the expensive optical equipment. There have been a few attempts to measure visibility by extracting depth and three-dimensional structure under bad weather conditions. However, if there are many movements of objects in the image, these approaches seem to be inappropriate. In addition, the realtime visibility measurement will require a relatively simple and fast processing. Typically the contrast degrades exponentially in the bad weather. Therefore, in this paper we propose an easy and quick method that extract contrast from images using a moving area filter and measure visibility by mathematically modelling of the relationship between image contrast and visibility. The moving area filter is used for removing the area of the sky and moving objects that affect visibility measurement on images. The method proposed here can make possible not only realtime visibility measurement from images taken by CCD cameras, but also steady visibility measurement by using the moving area filter in case of much traffic on the road.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.1-7
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• 2015

This research is to develop a possible process to apply k-means clustering to an efficient vehicle routing process under time varying vehicle moving speeds. Time varying vehicle moving speeds are easy to find in metropolitan area. There is a big difference between the moving time requirements of two specific delivery points. Less delivery times are necessary if a delivery vehicle moves after or before rush hours. Various vehicle moving speeds make the efficient vehicle route search process extremely difficult to find even for near optimum routes due to the changes of required time between delivery points. Delivery area division is designed to simplify this complicated VRPs due to time various vehicle speeds. Certain divided area can be grouped into few adjacent divisions to assume that no vehicle speed change in each division. The vehicle speeds moving between two delivery points within this adjacent division can be assumed to be same. This indicates that it is possible to search optimum routes based upon the distance between two points as regular traveling salesman problems. This makes the complicated search process simple to attack since few local optimum routes can be found and then connects them to make a complete route. A possible method to divide area using k-means clustering is suggested and detailed examples are given with explanations in this paper. It is clear that the results obtained using the suggested process are more reasonable than other methods. The suggested area division process can be used to generate better area division promising improved vehicle route generations.