• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.244-247
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• 2013

본 논문에서는 영상 통화나 웹캠 혹은 화상 회의 상황의 비디오 영상에서 안정적으로 사람 영역과 배경을 분리하는 방법을 제안한다. 이 방법은 카메라가 고정이라는 등의 제약을 두지 않고 자유롭게 움직이는 비디오 영상에서 사용자의 입력도 필요 없이 자동으로 사람 영역을 분리해 내게 된다. 첫 프레임에서 얼굴 검출을 통해 사람의 대략적인 위치를 추측하여 배경과 사람 영역을 Gaussian Mixture Model 로 모델링하고, 매 프레임 이 모델을 효율적으로 갱신한다. 그리고 비디오 영상의 연속성을 에너지 함수 설계에 적용하여 프레임간 사람 영역의 변화가 크지 않고 안정적으로 나오게 된다. 제안하는 방법은 기존 방법들에 비하여 제약이 적고, 사용자 입력이 필요 없으며 안정적으로 사람 영역을 분리함을 실험을 통하여 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.398-403
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• 2007

본 논문에서는 스테레오 비전을 이용하여 지하철 선로에 존재하는 장애물을 검출하는 기법을 제안한다. 본 논문에서는 두 대의 아날로그 카메라를 이용하여 좌우 영상을 획득하며, 영역 기반 정합을 이용하여 영상의 불균형을 계산한다. 불균형 영상의 배경을 매 프레임에서 갱신하고 현재 프레임의 불균형과의 차이에 의해 선로상의 장애물을 검출한다. 실내 역사에서 임의로 투척한 신문지, 상자, 인형과 임의로 진압한 사람을 모두 검출하였으며, 한 대의 카메라를 이용하는 기법에서 해결하기 어려운 배경과 비슷한 색상을 가지는 물체도 모두 검출하였다. 따라서 제안하는 기법은 선로상의 위험물을 검출하는 핵심 기술로 이용될 수 있다.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.245-248
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• 2002

This paper has been studied a new method to update the background image of a visual surveillance system which is not stationary. In order to do this, we use another background model designed with the whole monitored images in a regular time period. By comparing each changed area computed from the two background model images and current monitored image, the areas which will be updated are decided.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.85-94
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• 2004

Object tracking in a real time image is one of interesting subjects in computer vision and many Practical application fields during the past couple of years. But sometimes existing systems cannot find all objects by recognizing background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which is not influenced by illumination and to remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up Minimum Bounding Rectangle(MBR) using the internal point of detected object, the system tracks object through this MBR In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.415-418
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• 2008

This paper presents a method for moving objects detection, analysis and expression how much move as numerical value from the image which is captured by a network camera. To perform this method, we process few kinds of pre-processing to remove noise that are getting background image, difference image, binarization and so on. And to consider perspective effect, we propose modified ART2 algorithm. Finally, we express the result of ATR2 clustering as numerical value. This method is robust to size of object which is changed by perspective effect.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.468-471
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• 2008

이 논문은 응급상황을 인식하기 위하여 어안렌즈를 통해 획득된 영상을 이용하여 기절 동작을 인식하는 방법을 제안한다. 거실의 천장 중앙에 위치한 어안렌즈(fish-eye lens)를 장착한 카메라로부터 화각이 170인 RGB 컬러 모델의 어안 영상을 입력 받은 뒤, 가우시안 혼합 모델 기반의 적응적 배경 모델링 방법을 이용하여 동적으로 배경 영상을 갱신한다. 입력 영상의 평균 밝기를 구하고 평균 밝기가 급격하게 변화하지 않도록 영상 픽셀을 보정한 뒤, 입력 영상과 배경 영상과 차이가 큰 픽셀을 찾음으로써 움직이는 객체를 추출하였다. 그리고 연결되어 있는 전경 픽셀 영역들의 외곽점들을 추적하여 타원으로 매핑하고 움직이는 객체 영역의 형태를 단순화하였다. 이 타원을 추적하면서 어안 렌즈 영상을 투시 영상으로 변환한 다음 타원의 크기 변화, 위치 변화, 이동 속도 정보를 추출하여 이동과 정지 및 움직임이 기절동작과 유사한지를 판단하도록 하였다. 본 논문에서는 실험자로 하여금 기절동작, 걷기 동작, 앉기 동작 등 여러 동작을 취하게 하고 기절 동작 인식을 실험하였다. 실험 결과 어안 렌즈 영상을 그대로 사용하는 것보다 투시 영상으로 변환하여 타원의 크기변화, 위치변화, 이동속도 정보를 이용하는 것이 높은 인식률을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.222-228
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• 2010

In this paper, we propose a new approach to noise estimation incorporating spectral deviation with soft decision scheme to enhance the intelligibility of the degraded speech signal in non-stationary noisy environments. Since the conventional noise estimation technique based on soft decision scheme estimates and updates the noise power spectrum using a fixed smoothing parameter which was assumed in stationary noisy environments, it is difficult to obtain the robust estimates of noise power spectrum in non-stationary noisy environments that spectral characteristics of noise signal such as restaurant constantly change. In this paper, once we first classify the stationary noise and non-stationary noise environments based on the analysis of spectral deviation of noise signal, we adaptively estimate and update the noise power spectrum according to the classified noise types. The performances of the proposed algorithm are evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various ambient noise environments and show better performances compared with the conventional method.

• Geophysics and Geophysical Exploration
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• v.20 no.4
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• pp.199-206
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• 2017

Field data obtained from marine exploration are influenced by various environmental factors such as wind, waves, tidal current and flow velocity of a background medium. Most environmental factors except for the flow velocity are properly corrected in the data processing stage. In this study, the wave equation modeling considering flow velocity is used to generate observation data, and numerical experiments using the observation data were conducted to analyze the effect of flow velocity on waveform inversion. The numerical examples include the results with unrealistic flow velocities. In addition, an algorithm is suggested to numerically extract flow velocity for waveform inversion. The proposed algorithm was applied to the modified Marmousi2 model to obtain the results depending on the flow velocity. The effect of flow velocity on updated physical properties was verified by comparing the inversion results without considering flow velocity and those obtained from the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.92-101
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• 2005

It is difficult to detect the accurate detection which leads the camera it moves follows in change of the noise or illumination and Also, it could be recognized with backgound if the object doesn't move during hours. In this paper, the proposed method is updating changed background image as much as N＊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 moving by computing fixed distance pixel instead of operate all pixel. Also, set up minimum area of object to use boundary point of object abstracted through checking image pixel and motion detect of object. Therefore motion detection is available as is fast and correct without doing checking image pixel every Dame. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.70-77
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• 2017

Among available people counting methods, map-based approaches based on moving interest points have shown good performance. However, the stationary people counting is challenging in such methods since all static points of interest are considered as background. To include stationary people in counting, it is needed to discriminate between the static points of stationary people and the background region. In this paper, we propose a people counting method based on using both moving and static points. The proposed method separates the moving and static points by motion information. Then, the static points of the stationary people are classified using foreground mask processing and point pattern analysis. The experimental results reveal that the proposed method provides more accurate count estimation by including stationary people. Also, the background updating is enabled to solve the static point misclassification problem due to background changes.