• Journal of Industrial Technology
• /
• v.21 no.B
• /
• pp.91-98
• /
• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.4
• /
• pp.38-45
• /
• 2001

In this paper, we propose an algorithm for moving object tracking from moving camera using histogram back program intersection(HI) and XY-projection The proposed method segments objects using histogram back projection, matches tracing objects using histogram intersection and extracts them using XY- projection. Through the simulation this paper shows that the proposed method segments. matches and tracks objects without significant error image sequences obtained by moving camera.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.55-62
• /
• 2014

In the intelligent video surveillance system, moving objects generally are detected by calculating difference between background and input image. However formation of reliable background is known to be still challenging task because it is hard to cope with the complicated background. In this paper we propose an adaptive background formation algorithm considering stationary object. At first, the initial background is formed by averaging the initial N frames. Object detection is performed by comparing the current input image and background. If the object is at a stop for a long time, we consider the object as stationary object and background is replaced with the stationary object. On the other hand, if the object is a moving object, the pixels in the object are not reflected for background modification. Because the proposed algorithm considers gradual illuminance change, slow moving object and stationary object, we can form background adaptively and robustly which has been shown by experimental results.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.11
• /
• pp.79-88
• /
• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.5
• /
• pp.765-773
• /
• 2011

This paper proposes a control method for a differential robot to track and follow a moving object based on ultrasonic sensor. To track a target object, the method uses a transmitter and two receivers to get distances from the object. The method derives translational and rotational error by the distances and then it uses the errors to calculate control values based on PID control method. The control values are used to control the robot to follow moving object. The authors do some experimentations to analyze some characteristics such as influence of PID gain, influence of translational and rotational gain. This method not only can be applied for following moving object problem but also can be done group unit control problems.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.4
• /
• pp.346-360
• /
• 1992

This paper proposed a method of identity decision of moving object by the fuzzy inference. Moving object is extracted by the difference picture method and identity decision parameters of moving object are chosen area, perimeter. A/p ratio and vertex. In the brightness variation of 600 Lux-1600Lux, we determined the maximum error often parameters and then generated a fuzzified data. Fuzzified data are generated from the reference image and then decided the identity of extracted parameter by the fuzzy inference using fuzzy production rule. In order to verify validity of proposed method, experiment are performed with a toy car. As a result, it was shown that decision error was less than 4%

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2000.05a
• /
• pp.356-359
• /
• 2000

In this paper, we propose a model based moving object tracking algorithm in dynamic scene. To adapt the shape change of the moving object, the Hausdorff distance is applied as the measurement of similarity between model and image. To reduce the processing time, 2-D logarithmic search method is applied for locate the position of moving object. Experiments on a running motorcycle, the result showed that the mean square error of real position and tracking result is 1.845 and consequently, matching process is relatively simple and reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.371-373
• /
• 2015

This paper describes an analysis of hardware design conditions of moving object detection algorithm which is based on effective Gaussian mixture learning (EGML). The simulation model of EGML algorithm is implemented using OpenCV, and it is analyzed that the effects of parameter values on background learning time and moving object detection sensitivity for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-width parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.10 no.2
• /
• pp.101-106
• /
• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.