• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.199-202
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• 2003

An effective method is proposed for detecting, acquisition and tracking of a moving object using a disturbance map method in complex image sequences. A significant moving object is detected and tracked within the field of view by computing a modified disturbance map method between an Input image and a temporal average image. This method is very efficient in the serveillance application of digital CCTV and an automatic tracking camera. Experimental results using a real image sequence confirmed that the proposed method can effectively detect and track a significant moving object in complex image sequences.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1436-1440
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• 2010

In this paper, we proposed that stereo camera system using fuzzy control for moving object tracking. We extract some features of the moving object from overall image. This informations send to the PC and the PC calculate the coordinate of the object in the image frame. To make the object set in the middle of the image, the step motor should be controlled accurately and rapidly with the location information received by the PC. Then we design a fuzzy logic system for controlling stereo camera system. To verify the better performance of the proposed algorithm, we exemplified by experimental results.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2104-2107
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• 2003

In this paper, we propose the method Model based Non-Rigid Moving Object Tracking. Motion based method becomes difficult to predict precisely when motion gets larger, so that we can solve such difficultly with regarding the moving object as a model. In the model based method, it should be concerned about setting initial model and updating its model in each frame. We used SNAKE in a way to set the initial model, and also proposed a modified SNAKE to handle the previous SNAKE problems. Moreover, with the elliptical setting, we made the initializing process automatically which is highly subject to change in measuring the performance of SNAKE. We used the Hausdorff distance to identify models in each frame. Through our experiments, our Proposed algorithm does effective work in Non-Rigid Moving Object Tracking.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1992-1995
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• 2003

An adaptive active contour algorithm which shows stable object tracking performance under the moving or deformable environments, is proposed. In order to cope with local deformation of the object, an energy map is generated from the difference of the consecutive images and a new energy function based on the energy map is presented. The algorithm is evaluated on a set of artificial and real images to verify the efficiencies and test results show the stable tracking performance for the moving objects.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.741-748
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• 2001

In this paper, we propose a method of tracking and capturing a moving object by a mobile robot. The position of the moving object is acquired from the relation through color-based image information from a 2-DOF active camera mounted on the mobile robot. The direction and rotational angular velocity of the moving object are estimated using a state estimator. A Kalman fiber is used as the state estimator for taking characteristics of robustness against noises and uncertainties included in the input data. After estimating the trajectory of the moving object, we decide on the optimal trajectory and plan the motion of the mobile robot to capture the target object within the shortest distance and time. The effectiveness of the proposed method is demonstrated by the simulations and experiments.

• Journal of Information Display
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• v.6 no.4
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• pp.16-25
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• 2005

In this paper, a new 3D object tracking system using the disparity motion vector (DMV) is presented. In the proposed method, the time-sequential disparity maps are extracted from the sequence of the stereo input image pairs and these disparity maps are used to sequentially estimate the DMV defined as a disparity difference between two consecutive disparity maps Similarly to motion vectors in the conventional video signals, the DMV provides us with motion information of a moving target by showing a relatively large change in the disparity values in the target areas. Accordingly, this DMV helps detect the target area and its location coordinates. Based on these location data of a moving target, the pan/tilt embedded in the stereo camera system can be controlled and consequently achieve real-time stereo tracking of a moving target. From the results of experiments with 9 frames of the stereo image pairs having 256x256 pixels, it is shown that the proposed DMV-based stereo object tracking system can track the moving target with a relatively low error ratio of about 3.05 % on average.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.32-35
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• 2013

In this paper we proposed a PTZ camera system which automatically detect and track moving objects in the image. Once a moving object is detected the PTZ camera traces it in real-time. We proposed the control system which does not depend on camera focusing functionality but uses the object's center, moving direction, distance and speed. We implemented the system with the TI DM6446 DSP chip. The experimental result shows that the system has excellent performance for high speed vehicles.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3083-3085
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• 1999

This paper presents an algorithm that the mobile robot track accurately a moving object with information from a CCD camera mounted on mobile robot. Singular Value Decomposition is adapted to remove the measurement noise of a Raw data of CCD. The mobile robot estimate the trajectory using Kalman filter and track the path of a moving object with a servo motor. Computer simulation results are showed that the efficient tracking system for the mobile robot is designed properly.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.301-312
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• 2005

Three different cases should be considered for motion tracking: moving object with fixed camera, fixed object with moving camera and moving object with moving camera. Two methods are widely used for motion tracking: the optical flow method and the difference frame method. The optical new method is mainly used when either one, object or camera is fixed. This method tracks object using time-space vector which compares object position frame by frame. This method requires heavy computation, and is not suitable for real-time monitoring system such as DVR(Digital Video Recorder). The different frame method is used for moving object with fixed camera. This method tracks object by comparing the difference between background images. This method is good for real-time applications because computation is small. However, it is not applicable if the camera is moving. This thesis proposes and implements the motion tracking system using the difference frame method with PTZ(Pan-Tilt-Zoom) control. This system can be used for moving object with moving camera. Since the difference frame method is used, the system is suitable for real-time applications such as DVR.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.