• 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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• 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

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.

• 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 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 Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.1
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• pp.53-59
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• 2014

In multiple object tracking, accurate detection for each of objects that appear sequentially and effective tracking in complicated cases that they are overlapped with each other are very important. In this paper, we propose a multiple object tracking system that has a concrete detection and tracking characteristics by using multi-lateral histogram and SIFT feature extraction algorithm. Especially, by limiting the matching area to object's inside and by utilizing the location informations in the keypoint matching process of SIFT algorithm, we advanced the tracking performance for multiple objects. Based on the experimental results, we found that the proposed tracking system has a robust tracking operation in the complicated environments that multiple objects are frequently overlapped in various of directions.

• 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.

• 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 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.