• Journal of information and communication convergence engineering
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• 제12권3호
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• pp.193-197
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• 2014

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

• 정보처리학회논문지B
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• 제17B권1호
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• pp.15-22
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• 2010

비디오 감시 시스템에서 정확한 물체 추적을 위해서는 움직이는 물체가 없는 정적인 배경 영상이 필수적이다. 하지만 기존의 배경 생성 방법들은 주로 시간 축에 따른 화소 정보를 이용하여 오랫동안 정지해 있는 물체들이 존재하는 경우에는 적용하기 어려운 단점이 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 mean-shift와 fast marching method(FMM)을 이용해 시간 축 화소 정보와 공간 축 화소 정보를 이용하여 배경을 생성하는 방법을 제안한다. mean-shift를 이용해 시간 축에 따른 화소 값의 최빈값을 추정하여 배경을 생성하고, FMM을 이용해공간 축에 따른 화소 정보를 이용하여 일정 기간 동안 움직이지 않은 물체가 있는 환경에서 바람직한 배경을 생성한다. 실험 결과는 제안한 방법이 기존의 시간에 따른 빈도만을 이용하는 방법보다 더 효율적임을 보여준다.

• Journal of applied mathematics & informatics
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• 제15권1_2호
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• pp.511-525
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• 2004

we model communication and computer systems that process interactive and several and several types of background jobs. The scheduling policy in use is to share the processor among all interactive jobs and, at most, one background job of each type at a time according to the process sharing discipline. Background jobs of each type are served on a first-come-first-served basis. Such scheduling policy is called Processor Sharing with Background jobs (PSBJ). In fact, the PSBJ policy is commonly used on many communication and computer systems that allow interactive usage of the systems and process certain jobs in a background mode. In this paper, the stability conditions for the PSBJ policy are given and proved. Since an exact analysis of the policy seems to be very difficult, an approximate analytic model is proposed to obtain the average job sojourn times. The model requires the solution of a set of nonlinear equations, for which an iterative algorithm is given and its convergence is proved. Our results reveal that the model provides excellent estimates of average sojourn times for both interactive and background jobs with a few percent of errors in most of the cases considered.

• 한국정보전자통신기술학회논문지
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• 제2권3호
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• pp.31-38
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• 2009

A process of identifying moving objects from data is typical task in many computer vision applications. In this paper, we propose a motion segmentation method that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modelling. To demonstrate the effectiveness of proposed approach, we tested it gray-scale video data as well as RGB color space.

• 한국정보통신학회논문지
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• 제17권10호
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• pp.2419-2424
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• 2013

기존 MOG (Mixture of Gaussian) 알고리즘에서 배경 결정을 위한 작은 임계치는 주기적인 배경에서 배경 인식 지연을 발생시키고, 큰 임계치는 고정 배경에서 지나가는 객체를 배경으로 인식하게 한다. 본 논문은 적응적인 임계치를 이용한 개선된 MOG 알고리즘을 제안한다. 제안된 알고리즘은 MOG 알고리즘의 주도적인 배경 모델에서 가중치 변화를 단 장기적으로 평가하고, 배경을 정적 배경과 주기성 배경으로 분류하여 그들에 적절한 임계치를 설정한다. 실험결과들은 제안된 알고리즘이 정적 배경에서 기존 알고리즘과 등등한 성능을 유지하면서 주기성 배경에서 배경인식 지연의 최대 프레임수를 137에서 4로 줄여주는 것을 보여주고 있다.

• 한국비블리아학회지
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• 제7권1호
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• pp.293-317
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• 1994

The Purpose of this study is to verify the effect of the searcher's subject background on the result of online database searches. To achieve this purpose, an experimental method was adopted. 180 students performed online searches in the three different libraries chosen for this study. The subjects were classified into two groups according to the scores of the test. Data concerning processes, behavior, and results of the searches performed by the subjects in real situations were gathered. Immediately following the searches, the extent of their subject background were assessed through interview. The search effect consists of the 4 elements： search efficiency (the number of terms used per unit time), the number of relevant documents, the number of relevant documents per unit time, precision ratio. The major findings of this study are summarized as belows. 1. The searchers with strong subject background has significantly higher efficiency in searches made. Group A (of those with strong subject back-ground) use more search terms per unit time than Group B (of those with weak subject background) do. 2. In the searches made by those with strong subject background, more relevant documents art retrieved. 3. In the searches made by those with strong subject background, more relevant documents per unit time are retrieved. 4. The searchers with strong subject background has significantly higher precision ratio in searches made. In the searches made by those with strong subject background, more relevant documents of documents retrieved are retrieved.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.151-156
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• 2006

본 논문에서는 이동 카메라를 이용하여 얻은 영상에서 이동 물체 자동 검출 알고리즘을 위한 배경 에지 생성을 제시한다. 배경 영상은 삼각대 위에 고정시킨 카메라를 수평방향으로 회전하여 얻은 영상을 정렬시켜 재구성하여 만든다. 입력영상과 배경영상간의 에지 매칭 방법과 함께 강건한 파노라믹 배경 에지 생성을 위한 효율적인 방법을 제시한다. 제안한 알고리즘은 실제 영상 열에 적용하였다. 제안된 방법은 비디오 감시는 물론 침입자 검출과 같은 여러 감시 시스템에 성공적으로 이용될 수 있다.

• 한국컴퓨터정보학회논문지
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• 제20권7호
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• pp.17-24
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• 2015

In this paper, we propose a method to solve ghosting problem. To generate adaptive background, using an exponentially decreasing number of frames, may improve object detection performance. To extract moving objects from the background by using a differential image, detection error may be caused by object rotations or environmental changes. A ghosting problem can be issue-driven when there are outdoor environmental changes and moving objects. We studied that a differential image by adaptive background may reduce the ghosting problem. In experimental results, we test that our method can solve the ghosting problem.

• 정보학연구
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• 제10권4호
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.