• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.35-45
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• 2008

Realtime visibility measurement using camera is a new method which can collect some realtime visibility data similar to those of the human eyes, and can replace the existing methods using the expensive optical equipment. There have been a few attempts to measure visibility by extracting depth and three-dimensional structure under bad weather conditions. However, if there are many movements of objects in the image, these approaches seem to be inappropriate. In addition, the realtime visibility measurement will require a relatively simple and fast processing. Typically the contrast degrades exponentially in the bad weather. Therefore, in this paper we propose an easy and quick method that extract contrast from images using a moving area filter and measure visibility by mathematically modelling of the relationship between image contrast and visibility. The moving area filter is used for removing the area of the sky and moving objects that affect visibility measurement on images. The method proposed here can make possible not only realtime visibility measurement from images taken by CCD cameras, but also steady visibility measurement by using the moving area filter in case of much traffic on the road.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.254-258
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• 1996

In this paper, we proposed realtime visual tracking system of moving object for 2D target using extended Kalman Filter Algorithm. A targeting marker are recongnized in each image frame and positions of targer object in each frame from a CCD camera while te targeting marker is attached to the tip of the SCARA robot hand. After the detection of a target coming into any position of the field-of-view, the target is tracked and always made to be located at the center of target window. Then, we can track the moving object which moved in inter-frames. The experimental results show the effectiveness of the Kalman filter algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of Wind Energy
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• v.10 no.3
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• pp.5-11
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• 2019

This paper suggests and compares two algorithms, a moving average filter method and a method developed by the National Renewable Energy Laboratory (NREL), to verify the yaw control algorithm characteristic to reduce yaw error for a wind turbine. A characteristic change for yaw movement in accordance with control parameter change that consists of each control method has been verified. Also, yaw simulations were performed using nacelle wind data measured from two areas with different turbulence intensities and the yaw movement data in each area was compared. These two algorithms and real data were compared by calculating mean absolute error (MSE) and the number of yawing (NY). As a result of the analysis, the MSE values were not significantly different between the two algorithms, but the algorithm proposed by the NREL was found to reduce yaw movement by up to 50 percent more than the moving average filter method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.641-647
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• 2015

This paper proposes a new strategy for a quad-rotor to track a moving object efficiently by using image processing and an extended Kalman filter. The goal of path planning for the quad-rotor is to design an optimal path from the start point to the destination point. To lengthen the freight time of the quad-rotor, an optimal path is required to reduce the energy consumption. To track a moving object, the mark signed on the moving object has been detected by a camera mounted first on the quad-rotor. The center coordinates of the mark and its area are calculated through the blob analysis which is one type of image processing. The mark coordinates are utilized to obtain information on the motion direction and the area of the mark is utilized to recognize whether the object moves backward or forward from the camera on the quad-rotor. In addition, an extended Kalman filter has been applied to predict the direction and speed of the dynamically moving object. Through these schemes, it is aimed that the quad-rotor can track the dynamic object efficiently in terms of flight distance and time. Through the two different route freights of the quad-rotor, the performance of the proposed system has been demonstrated.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.747-752
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• 2011

The position detection of overlapping area in the interframe for image stitching using auto and cross correlation function (ACCF) and compounding one image with the stitching algorithm is presented in this paper. ACCF is used by autocorrelation to the featured area to extract the filter mask in the reference (previous) image and the comparing (current) image is used by crosscorrelation. The stitching is detected by the position of high correlation, and aligns and stitches the image in shifting the current image based on the moving vector. The ACCF technique results in a few computations and simplicity because the filter mask is given by the featuring block, and the position is enabled to detect a bit movement. Input image captured from CMOS is used to be compared with the performance between the ACCF and the window correlation. The results of ACCF show that there is no seam and distortion at the joint parts in the stitched image, and the detection performance of the moving vector is improved to 12% in comparison with the window correlation method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.361-369
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• 2002

In order to extract motion features from an input image acquired by a static CCD-camera in a restricted area, we need a robust algorithm to cope with noise sensitivity and condition change. In this paper, we proposed an efficient algorithm to extract and track motion features in a noisy environment or with sudden condition changes. We extract motion features by considering a change of neighborhood pixels when moving objects exist in a current frame with an initial background. To remove noise in moving regions, we used a morphological filter and extracted a motion of each object using 8-connected component labeling. Finally, we provide experimental results and statistical analysis with various conditions and models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.425-428
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• 2008

To overcome the limitation of tracking speed on signal-shaded area and the discontinuity of GPS, we present a system which estimates the speed of moving object using GPS and accelerometer. This system is designed to correct accelerometer's noises which are caused by vibration and impact to the object and errors of itself, from the navigation information of GPS receiver and accelerometer which are installed to moving object. And using this information, it estimates the speed of moving object on GPS signal-shaded area to complement discontinuity of GPS navigation information. We designed Kalman Filter structure using GPS and accelerometer to apply this system, and verify that the system can estimate object's speed on GPS signal-shaded area. Finally, we present the possibilities applying to telematics systems like automatic navigation system.