• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.563-572
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• 2020

Tidal flats provide important indicators that inform the condition of the environment, so we need to monitor them systematically. Currently, the projects to monitor tidal flats by periodically observing the creatures in tidal flats are underway. Still, it is done in a way that people observe directly, so it is not systematic and efficient. In this paper, we propose a background modeling method for tidal flat images that can be applied to a system that automatically monitors creatures living in tidal flats using sensor network technology. The application of sensor network technology makes it difficult to collect enough images due to the limitation of transmission capacity. Therefore, in this paper, we propose a method to effectively model the background and generate foreground maps by reflecting the characteristics of tidal flat images in the situation where the number of images to be used for analysis is small. Experimental results show that the proposed method models the background of a tidal flat image easily and accurately.

• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.307-309
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• 2006

움직이는 오브젝트(Object)를 추출하기 위한 배경 제거(Background Subtraction) 단계는 실시간 감시시스템(Real-time Surveillance System)에서 중요한 과정 중에 하나이다. 배경 제거를 효과적으로 진행하기 위한 배경을 모델링, 배경 유지 보수 방법이 존재하는데, 효율성이 높은 방법으로 적응적 가우시안 혼합 배경 모델링(Adaptive Gaussian Mixture Background Modeling)이 제시되고 있다. 본 논문에서는 이기법을 바탕으로 하여 이러한 실시간 배경 모델링 시스템을 구현하려 하고, 중앙 처리 장치(CPU)가 아닌 그래픽 처리 장치(Graphic Processing Units : GPU)를 사용하여 보다 향상된 방범을 구현함으로서 관련사항을 제안하려 한다.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.372-378
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• 2015

Background subtraction is the first processing stage in video surveillance. It is a general term for a process which aims to separate foreground objects from a background. The goal is to construct and maintain a statistical representation of the scene that the camera sees. The output of background subtraction will be an input to a higher-level process. Background subtraction under dynamic environment in the video sequences is one such complex task. It is an important research topic in image analysis and computer vision domains. This work deals background modeling based on modified adaptive Gaussian mixture model (GMM) with three temporal differencing (TTD) method in dynamic environment. The results of background subtraction on several sequences in various testing environments show that the proposed method is efficient and robust for the dynamic environment and achieves good accuracy.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.159-161
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• 2004

Many motion object detection algorithms rely on the process of background subtraction, an important technique that is used for detecting changes from a model of the background scene. This paper propose a novel method to update the background model image of a visual surveillance system which is not stationary. In order to do this, we use a background model based on statistical qualities of monitored images and another background model that excluded motions. By comparing each changed area computed from the two background model images and current monitored image, the areas that will be updated are decided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.344-352
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• 2003

An effective algorithm for implementation of which detects moving object from image sequences. predicts the direction of it. and drives the camera in real time is proposed. In static camera, for robust motion detection from a dynamic background scene, the proposed algorithm performs statistical modeling of moving objects and background, and trains the statistical modeling of moving objects and background, and trains the statistical feature of background with the initial parts of sequence which have no moving objects. Active camera moving objects are segmented by following procedure, an improved order adaptive lattice structured linear predictor is used. The proposed algorithm shows robust object tracking results in the environment of static or active camera. It can be used for the unmanned surveillance system, traffic monitoring system, and autonomous vehicle.

• ETRI Journal
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• v.45 no.5
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• pp.795-810
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• 2023

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

• Journal of Broadcast Engineering
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• v.10 no.2
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• pp.156-165
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• 2005

This paper presents a new auto brighoess control algorithm fur adaptive background subtraction. The algorithm is designed to cope with the problem of auto-brightness adjustment feature of consumer-type cameras. The experimental results show the proposed method improves performance of the classification. This will be beneficial to many computer vision applications in term of reducing the cost of implementation and making them more available to the mass consumer market.

• The Mathematical Education
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• v.55 no.2
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• pp.233-249
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• 2016

In this study, we extracted the background meaning of the condition that four points lie on a circle, analyzed textbooks critically and proposed the orientation to improve the content in the textbook. As results, the condition has a realistic background meaning which is 'mathematical modeling of finding a fair location'. The condition has a mathematical background meanings which are 'a first complex situation distinguished from two points and three points', 'the condition described in the perspective of side and angle in order to overcome the disadvantages of the perpendicular bisectors context' and 'being possible to transfer more than five points'. However it is difficult to understand the reason why the condition is on four points in the current textbook. In addition, it is difficult to recognize the connectivity of a circumcenter of triangle. To overcome these problems, we proposed five orientations to improve the content in the textbook.