• 전기학회논문지
• /
• 제65권3호
• /
• pp.477-486
• /
• 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.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
• /
• pp.281-284
• /
• 2003

본 연구에서는 이동 객체 추적 시스템의 전처리 과정으로 움직임 영역을 검출하는 방법을 제안한다. 연속되는 영상으로부터 시간적으로 차이가 있는 두 개의 프레임을 얻은 후 이들의 이진 차영상을 구함으로서 움직임 영역을 검출한다. 차영상을 이용하는 경우 이전 프레임에서의 객체 영역과 현재 프레임에서의 객체 영역이 모두 검출된다. 추적 시스템에서는 카메라의 이동에 따라 배경이 변화되기 때문에 어느 영역이 객체의 현재 위치인지를 결정하는 방법이 필요하다. 이를 위해 본 연구에서는 현재 프레임의 이진 에지영상을 구하고 이것을 차영상과 논리적인 AND 연산을 수행한다. 실험 결과 이동 객체의 움직임 영역을 정확히 검출할 수 있음을 확인할 수 있었다.

• 한국실내디자인학회논문집
• /
• 제25권2호
• /
• pp.21-29
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제7권9호
• /
• pp.1240-1245
• /
• 2004

본 논문에서는 움직임 영역의 추적 및 움직임 물체의 추출을 위한 알고리즘을 제안한다. 제안한 알고리즘에서는 카메라의 움직임이 고정되어있는 감시카메라나 비디오폰과 같은, 배경이 고정된 시스템으로 가정하였다. 제안된 움직임 영역검색 알고리즘을 이용하여 움직임부분을 먼저 찾은 후, 움직임영역 안에서 다시 움직임 물체만을 분할하는 기법을 제안하였다. 제안한 알고리즘은 노이즈에 대해 보다 강인한 특성을 가지며 움직임영역의 추적 및 추출이 효율적으로 수행되었다.

• 가정과삶의질연구
• /
• 제22권6호
• /
• pp.1-11
• /
• 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.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2004년도 학술대회지
• /
• pp.823-826
• /
• 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.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2004년도 춘계종합학술대회
• /
• pp.591-594
• /
• 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.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권3호
• /
• pp.321-329
• /
• 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.

• 한국인터넷방송통신학회논문지
• /
• 제8권3호
• /
• pp.35-45
• /
• 2008

카메라를 이용한 실시간 시정측정은 인간의 시정 감각과 유사하고 현실성 있는 시정자료를 획득할 수 있으며 기존 고가의 광학기기를 이용한 측정방법을 대체할 수 있는 새로운 측정방식이다. 영상으로부터 깊이정보나 3차원 구조의 추출 등을 통해 시정을 측정하려는 시도가 있으나, 단순하고 빠른 처리가 요구되는 실시간 시정측정과 이동물체가 많이 나타나는 경우에는 많은 문제점을 갖고 있다. 본 논문에서는 시정의 감소는 영상에서 지수적인 대비의 감소로 나타난다는 점에 착안하여 영상으로부터 이동영역 필터를 이용하여 대비를 추출하고 영상대비와 시정간의 상관관계를 수학적으로 모델링함으로써 쉽고 빠르게 시정을 측정할 수 있는 방법을 제안한다. 이동영역 필터는 영상으로부터 시정측정에 영향을 주는 하늘과 물체의 이동영역을 효과적으로 제거하기 위해 사용된다. 제안된 방법은 카메라를 통해 입력된 영상으로부터 실시간 시정측정이 가능할 뿐만 아니라 도로와 같이 차량의 이동이 많은 경우에도 안정적인 시정측정이 가능하다.

• 산업경영시스템학회지
• /
• 제38권3호
• /
• pp.1-7
• /
• 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.