• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1377-1381
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• 2003

This paper describes real time object tracking of 3D objects in 2D image sequences. The moving objects are segmented from the image sequence using morphological operations. The moving objects are segmented by the method of differential image followed by the process of morphological dilation. The moving objects are recognized and tracked using statistical moments. The direction of moving objects are determined by the Hough transform. The straight lines in the moving objects are found with the help of Hough transform. The direction of the moving object is calculated from the orientation of the straight lines in the direction of the principal axes of the moving objects. The direction of the moving object and the displacement of the object in the image sequence is used to calculate the velocity of the moving objects. The simulation results of the proposed method are promising on the test images.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.99.3-99
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• 2002

1. Introduction 2. Detection and Tracking of Moving Objects 3. Objects Tracking and Experiment 4. Conclusion

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.823-826
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• 2004

In this paper, A tracking of the moving objects using normalized hue distribution in HSI color model was proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area. Hue information of the detected moving area are normalized by 24 levels from $0^{\circ}$ to $3600^{\circ}A$ distance in between normalized levels with a hue distribution chart of the normalized moving objects is used for the identity distinction feature parameters of the moving objects. To examine proposed method in this paper, image of moving cars are obtained by setting up three cameras at different places every 1 km on outer motorway. The simulation results of identity distinction show that it is possible to distinct the identity a distance in between normalization levels of a hue distribution chart without background.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.99-110
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• 2009

Currently, there are many applications being developed based on sensor network technology. A tracking method for moving objects in sensor network is one of the main issue of this field. There is a little research on this issue, but most of the existing work has two problems. The first problem is a communication overhead for visiting sensor nodes many times to track a moving object. The second problem is an disability for dealing with many moving objects at a time. To resolve the problems, we, in this paper, propose a signature-based tracking method using efficient data aggregation for moving objects, called SigMO-TRK. For this, we first design a local routing hierarchy tree to aggregate moving objects' trajectories efficiently by using a space filtering technique. Secondly, we do the tracking of all trajectories of moving objects by using signature in a efficient way, our approach generates signatures to method. In addition, by extending the SigMO-TRK, we can retrieve the similar trajectories of moving objects for given a query. Finally, by using the TOSSIM simulator, we show that our signature-based tracking method outperforms the existing tracking method in terms of energy efficiency.

• 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.65 no.3
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.979-994
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• 2008

Moving objects are spatio-temporal data that change their location or shape continuously over time. Generally, if continuously moving objects are managed by a conventional database management system, the system cannot properly process the past and future location which is not stored in the database. Up to now, for the purpose of location tracking which is not stored, the linear interpolation to estimate the past location has been usually used. It is suitable for the moving objects on linear route, not curved route. In this paper, we propose a past location tracking algorithm for a moving object on curved routes, and also suggest a future location tracking algorithm using some past location information. We found that the proposed location tracking algorithm has higher accuracy than the linear interpolation function.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.427-435
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• 2002

This paper presents a development result for image processed tracking system of multiple moving objects based on Kalman filter and a simple window tracking method. The proposed algorithm of foreground detection and background adaptation (FDBA) is composed of three modules: a block checking module(BCM), an object movement prediction module(OMPM), and an adaptive background estimation module (ABEM). The BCM is processed for checking the existence of objects. To speed up the image processing time and to precisely track multiple objects under the object's mergence, a concept of a simple window tracking method is adopted in the OMPM. The ABEM separates the foreground from the background in the reset simple tracking window in the OMPM. It is shown through experimental results that the proposed FDBA algorithm is robustly adaptable to the background variation in a short processing time. Furthermore, it is shown that the proposed method can solve the problems of mergence, cross and split that are brought up in the case of tracking multiple moving objects.

• 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.