• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.319-325
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• 2002

Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Especially, estimating and identifying moving objects, when the background and moving objects vary dynamically, are very difficult. It is possible under such a complex environment that targets may disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. In this paper, we propose another version of the Kalman filter, to be called structured Kalman filter, which can successfully work its role of estimating motion information under a deteriorating condition such as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.45-51
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• 2006

Recently, the IDS(Intrusion Detection System) using a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in the environment with abrupt illumination change. In this paper, we propose an effective moving edge extraction algerian from a sequence image. The proposed algorithm extracts edge segments from current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of moving object in the noisy environment with abrupt illumination change.

• Journal of Broadcast Engineering
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• v.4 no.1
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• pp.22-31
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• 1999

Moving object tracking improves the efficiency and qualification for observation system, broadcasting system, video conference, etc. This paper propcses an improved Bayes decision method for detecting and tracking moving objects in active camera environment. The Bayes decision based tracking approach finds the region of moving objects by analyzing the image sequences statistically. The propcsed algorithm regenerates the probability density function to accord with moving objects and background for active camera. Experimental results show that the algorithm is accurate. reliable and noise resistant. The result is compared with those of the conventional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.521-525
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• 2006

본 논문에서는 초음파 및 적외선 센서와 무선 카메라를 장착한 소형 이동 로봇의 장애물 회피 및 물체 추적 방법을 제시한다. 장애물 회피를 위해서 제어부의 초음파 발생 신호의 귀환시간과 거리와의 관계 및 적외선 센서에서 측정한 아날로그신호와 거리와의 관계를 추출하여 이동 로봇과 물체와의 거리를 판단하여 로봇의 움직임을 제어하는데 사용한다. 물체 추적 모드에서는 첫째, 물체와 배경 및 유사잡음들과의 강인한 분리를 위하여 고유색상정보와 움직임 정보 등의 사전정보를 활용하였으며 둘째, 형태의 변화가 수반되는 경우에도 유연한 대처능력을 갖도록 하기 위해 영상의 영역분할 방법을 통해 모든 후보영역내의 물체의 존재를 확인하고 물체영역만을 추출하였다. 셋째, 물체 형태정보함수를 정의하고 해당함수를 형태의 보전 에너지로 활용하여 동일 물체의 대응문제를 효과적으로 해결하였다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.28-34
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• 1998

In this paper, I'd like to make a suggestion for boundary line detect algorithm which is used 3-D image processing system in order to track moving object. Through this study, more than anything else, difference image method was adopted to detect moving object in input image. To detect moving object, I made use of detect windows constructed by 4's predictive areas and object area for the purpose of reducing processing time and its size was determined by the size of moving object and prediction parameter directed center position. And also, tracking camera was movable toward the direction of X, Y by DC motor. As a conclusion of the study proposed algorithm, I found out the following results that tracking error was less than 6% of total moving object size and maximum tracking time 2 seconds by toy-car simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.867-868
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• 2013

본 논문은 물류창고와 같은 실내 공간에서 RFID와 초음파 센서등을 이용하여 이동로봇이 자율적으로 자신의 위치를 파악하고 관리자가 지정한 목표 물체를 인식하여 간단한 업무를 보조할 수 있는 기법을 제안한다. 실내공간엔 RFID를 지면과 목표물체에 설치하고 로봇은 RFID의 리더기와 물체 접근시 활용이 가능한 추가적 센서를 갖춤으로써 이동시 자기 위치를 실시간으로 파악하고 물체로부터도 고유정보를 얻는다. 초음파 센서 신호의 귀환시간을 활용하여 근접한 물체와의 상대 거리를 추출하고 바닥의 RFID로부터 이미 획득한 자기 위치를 조합하여 목표 물체의 절대 위치를 구한다. 이는 이동 로봇을 중심으로 한 경로지도를 실시간으로 작성하며 동시에 실내의 이동 가능 구조 및 목표 물체의 파악이 가능하여 이동로봇의 자율적 탐색을 위한 최적 경로 계획 수립에 활용 가능하다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.137-145
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• 2004

In this paper, a new algorithm for the segmentation of the multi moving objects at the 3 dimension space and the method of measuring the distance from the camera to the moving object by using stereo video monitoring system is proposed. It get the input image of left and right from the stereo video monitoring system, and the area of the multi moving objects segmented by using adaptive threshold and PRA(pixel recursive algorithm). Each of the object segmented by window mask, then each coordinate value and stereo disparity of the multi moving objects obtained from the window masks. The distance of the multi moving objects can be calculated by this disparity, the feature of the stereo vision system and the trigonometric function. From the experimental results, the error rate of a distance measurement be existed within 7.28％, therefore, in case of implementation the proposed algorithm, the stereo security system, the automatic moving robot system and the stereo remote control system will be applied practical application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.364-370
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• 2011

This paper propose a novel method for tracking moving object based on covariance matrix and Riemannian Manifolds. With image backgrounds continuously changed, we use the covariance matrices to extract features for tracking nonrigid object undergoing transformation and deformation. The covariance matrix can make fusion of different types of features and has its small dimension, therefore we enable to handle the spatial and statistical properties as well as the component correlation. The proposed method can estimate the position of the moving object by employing the covariance matrix of object region as a feature vector and comparing the candidate regions. Rimannian Geometry is efficiently adapted to object deformation and change of shape and improve the accuracy by using geodesic distance to predict the estimated position with the minimum distance. The experimental results have shown that the proposed method correctly tracked the moving object.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.