• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.359-368
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• 2002

In this paper, we propose effective boundary line extraction algorithm for moving objects by matching error image and moving vectors, and fast tracking algorithm for moving object by partial boundary lines. We extracted boundary line for moving object by generating seeds with probability distribution function based on Watershed algorithm, and by extracting boundary line for moving objects through extending seeds, and then by using moving vectors. We processed tracking algorithm for moving object by using a part of boundary lines as features. We set up a part of every-direction boundary line for moving object as the initial feature vectors for moving objects. Then, we tracked moving object within current frames by using feature vector for the previous frames. As the result of the simulation for tracking moving object on the real images, we found that tracking processing of the proposed algorithm was simple due to tracking boundary line only for moving object as a feature, in contrast to the traditional tracking algorithm for active contour line that have varying processing cost with the length of boundary line. The operations was reduced about 39% as contrasted with the full search BMA. Tracking error was less than 4 pixel when the feature vector was $(15\times{5)}$ through the information of every-direction boundary line. The proposed algorithm just needed 200 times of search operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.338-347
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• 2005

In this paper, a decimation-in-time search direction algorithm for displacement prediction of moving object is proposed. The initialization of the proposed algorithm for moving direction prediction is performed by detecting moving objects at sequential frames and by obtaining a moving angle and a moving distance. A moving direction of the moving object at current frame is obtained by applying the decimation-in-time search direction mask. The decimation-in-tine search direction mask is that the moving object is detected by thinning out frames among the sequential frames, and the moving direction of the moving object is predicted by the search mask which is decided by obtaining the moving angle of the moving object in the 8 directions. to examine the propriety of the proposed algorithm, velocities of a driving car are measured and tracked, and to evaluate the efficiency, the proposed algorithm is compared to the full search algorithm. The evaluated results show that the number of displacement search times is reduced up to 91.8$\%$ on the average in the proposed algorithm, and the processing time of the tracking is 32.1ms on the average.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.207-209
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• 2009

다양한 센서 및 영상 카메라를 이용한 교통, 보안 및 안전 감시 시스템에 있어 처리해야 하는 영상 데이터의 양은 점점 커져가고 있다. 또한 단일 카메라가 아닌 많은 수의 카메라를 이용할 경우 운영자가 모든 영상 데이터를 확인하고 이에 대한 응답을 즉시 하기가 힘이 든다. 따라서 영상 데이터를 이용하기 위한 시스템에서 소프트웨어적인 처리는 필수이며 물체를 정확하게 추적하기 위해서는 물체를 인식하고 물체의 움직임을 예측하고 움직임을 보정하는 단계가 필요하다. 본 논문에서는 물체의 움직임을 정확히 추적하기 위하여 이동 물체를 추적할 때에 적절한 Kalman 필터를 이용하여 고속 물체 추적 시스템을 구현하였다.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.619-628
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• 2013

In this paper, we describe a strongly improved tracking method using combination of CAMshift and Kalman filter algorithm. CAMshift algorithm doesn't consider the object's moving direction and velocity information when it set the search windows for tracking. However if Kalman filter is combined with CAMshift for setting the search window, it can accurately predict the object's location with the object's present location and velocity information. By using this prediction before CAMshift algorithm, we can track fast moving objects successfully. Also in this research, we show better tracking results than conventional approaches which make use of single color information by using both color information of HSV and YCrCb simultaneously. This modified approach obtains more robust color segmentation than others using single color information.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.707-714
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• 2002

This paper presents a real-time Hausdorff matching algorithm for tracking of moving object acquired from an active camera. The proposed method uses the edge image of object as its model and uses Hausdorff distance as the cost function to identify hypothesis with the model. To enable real-time processing, a high speed approach to calculate Hausdorff distance and half cross matching method to improve performance of existing search methods are also presented. the experimental results demonstrate that the proposed method can accurately track moving object in real-time.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.33-47
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• 2002

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.181-191
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• 2002

Video based surveillance systems based on an active camera require a fast algorithm for real time detection and tracking of local motion in the presence of global motion. This paper presents a new fast and efficient motion detection and tracking algorithm using the displaced frame difference (DFD). In the Proposed algorithm, first, a Previous frame is adaptively selected according to the magnitude of object motion, and the global motion is estimated by using only a few confident matching blocks for a fast and accurate result. Then, a DFD is obtained between the current frame and the selected previous frame displaced by the global motion. Finally, a moving object is extracted from the noisy DFD by utilizing the correlation between the DFD and current frame. We implement this algorithm into an active camera system including a pan-tilt unit and a standard PC equipped with an AMD 800MHz processor. The system can perform the exhaustive search for a search range of 120, and achieve the processing speed of about 50 frames/sec for video sequences of 320$\times$240. Thereby, it provides satisfactory tracking results.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.51-52
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• 2015

동영상에서의 객체추적을 위해 주파수변환을 적용하는 연구가 발표되고 있다. 주파수영역으로의 변환 방법은 FFT와 같은 고속변환을 적용하므로 실시간 객체 추적을 위해 좋은 방법이다. 동영상에서 이동 중인 객체는 인접 프레임에서 위치의 변화가 크지 않기 때문에 주파수영역으로의 변환 방법으로 고속 객체 탐색을 실현할 수 있다. 그러나 동영상에서 이동중인 객체는 형상이 조금씩 변할 수 있으므로 탐색기법은 이 점을 고려해야한다. 본 논문에서는 추적 대상 객체가 다른 물체에 의해 가려지는 상황에 따라 필터갱신을 적응적으로 수행하고 이동경로와 주변정보를 사용하고 검출 객체에 비례대칭평균화 전처리를 적용함으로써 추적 대상객체가 가려지는 상황에서도 추적 실패를 줄일 수 있는 객체 탐색 기법을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.636-644
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• 2015

This research aims to suggest methods to improve object tracking performance by combining CAM shift algorithm with 8-way search window, and reduce arithmetic operation by reducing the number of frame used for tracking. CAM shift has its adverse effect in tracking methods using signature color or having difficulty in tracking rapidly moving object. To resolve this, moving search window of CAM shift makes it possible to more accurately track high-speed moving object after finding object by conducting 8-way search by using information at a final successful timing point from a timing point missing tracking object. Moreover, hardware development led to increased unnecessary arithmetic operation by increasing the number of frame produced per second, which indicates efficiency can be enhanced by reducing the number of frame used in tracking to reduce unnecessary arithmetic operation.