• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.16-22
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• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.786-792
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• 2007

Moving object tracking is very important in video surveillance system. This paper presents a method for tracking moving objects in an outdoor environment. To moving object tracking, first, after extract object that move yielding weight subtraction image and then use close operator to reduce the noise. And we track a object that move detected by matching the extracted multi-feature information. The proposed tracking technique can track moving object by multi-feature matching method so that exactly tracking the objects which are suddenly move or stop. The proposed tracking technique can be efficiently tracking the moving objects, because of combined with spatial position, shape and intensity informations.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.766-770
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• 2010

In this paper, we present an effective covariance tracking algorithm based on adaptive size changing of tracking window. Recent researches have advocated the use of a covariance matrix of object image features for tracking objects instead of the conventional histogram object models used in popular algorithms. But, according to the general covariance tracking algorithm, it can not deal with the scale changes of the moving objects. The scale of the moving object often changes in various tracking environment and the tracking window(or object kernel) has to be adapted accordingly. In addition, the covariance matrix of moving objects should be adaptively updated considering of the tracking window size. We provide a solution to this problem by segmenting the moving object from the background pixels of the tracking window. Therefore, we can improve the tracking performance of the covariance tracking method. Our several simulations prove the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.157-160
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• 2008

A realization for moving object detecting and tracking system in two dimensional plane using stereo line CCDs and lighting source is presented in this paper. Instead of processing camera images directly, two line CCD sensor and input line image is used to measure two dimensional distance by comparing the brightness on line CCDs. The algorithms are used the moving object tracking and coordinate converting method. To ensure the effective detection of moving path, a detection algorithm to evaluate the reliability of each measured distance is developed. The realized system results are that the performance of moving object recognizing shows 5mm resolution and mean error is 1.89%, and enables to track a moving path of object per 100ms period.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.106-111
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• 2011

Video-based detection and tracking of moving objects has been widely used in real-time monitoring systems and a videoconferencing. Also, because object motion tracking can be expanded to Human-computer interface and Human-robot interface, Moving object tracking technology is one of the important key technologies. If we can track a specified object in an environment of multiple moving objects, then there will be a variety of applications. In this paper, we introduce a specified object motion tracking using particle filter. The results of experiments show that particle filter can achieve good performance in single object motion tracking and a specified object motion tracking in an environment of multiple moving objects.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.678-681
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• 2004

In this paper, efficient tracking of a moving object using optimal representative blocks is implemented by a mobile robot with a pan-tilt camera. The key idea comes from the fact that when the image size of moving object is shrunk in an image frame according to the distance between the camera of mobile robot and the moving object, the tracking performance of a moving object can be improved by changing the size of representative blocks according to the object image size. Motion estimation using Edge Detection(ED) and Block-Matching Algorithm(BMA) is often used in the case of moving object tracking by vision sensors. However these methods often miss the real-time vision data since these schemes suffer from the heavy computational load. In this paper, the optimal representative block that can reduce a lot of data to be computed, is defined and optimized by changing the size of representative block according to the size of object in the image frame to improve the tracking performance. The proposed algorithm is verified experimentally by using a two degree-of-freedom active camera mounted on a mobile robot.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.697-704
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• 2003

In this paper, the visual tracking system for arbitrary shaped moving object is proposed. The established tracking system can be divided into model based method that needs previous model for target object and image based method that uses image feature. In the model based method, the reliable tracking is possible, but simplification of the shape is necessary and the application is restricted to definite target mod el. On the other hand, in the image based method, the process speed can be increased, but the shape information is lost and the tracking system is sensitive to image noise. The proposed tracking system is composed of the extraction process that recognizes the existence of moving object and tracking process that extracts dynamic characteristics and shape information of the target objects. Specially, active contour model is used to effectively track the object that is undergoing shape change. In initializatio n process of the contour model, the semi-automatic operation can be avoided and the convergence speed of the contour can be increased by the proposed effective initialization method. Also, for the efficient solution of the correspondence problem in multiple objects tracking, the variation function that uses the variation of position structure in image frame and snake energy level is proposed. In order to verify the validity and effectiveness of the proposed tracking system, real time tracking experiment for multiple moving objects is implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.4
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• pp.349-354
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• 2005

The study on computer vision is aimed on creating a system to substitute the ability of human visual sensor. Especially, moving object tracking system is becoming an important area of study. In this study, we have proposed the tracking system of single moving object based on motion estimation. The tracking system performed motion estimation using differential image, and then tracked the moving object by controlling Pan/Tilt device of camera. Proposed tracking system is devided into image acquisition and preprocessing phase, motion estimation phase and object tracking phase. As a result of experiment, motion of moving object can be estimated. The result of tracking, object was not lost and tracked correctly.