• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.45-56
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• 2007

Automatic tracking of vehicles is important to accurately estimate the traffic information including vehicle speeds in video-based traffic measurement systems. Because of the limited field of view, the range of visual tracking with a single camera is restricted. In order to enlarge the tracking range for better chance of monitoring the vehicle behaviors, a tracking with consecutive multiple video sequences is necessary. This parer proposes a carefully designed rule-based vehicle racking scheme and apply it for the tracking for two well synchronized video sequences. In the scheme, almost all possible cases that can appear in the video-based vehicle tracking are considered to make rules. Also, the rule based scheme is augmented with Kalman filter. The result of tracking can be successfully used to collect data such as temporal variation of vehicle speed and behavior of individual vehicle behaviors in the enlarged tracking region.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.294-299
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• 2006

This paper studies a visual feedback control scheme for robot manipulators with camera-in-hand configurations. We design a robust controller that compensates for bounded parametric uncertainties of robot mechanical dynamics. In order to reduce steady state tracking error of the robot arms due to uncertain dynamics, integral action is included in the control input. Using the Lyapunov stability criterion, the uniform ultimate boundedness of the tracking error is proved. Simulation and experimental results with a 2-1ink robot manipulator illustrate the robustness and effectiveness of the proposed control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1816-1819
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• 1997

Using the vision system, robotic tasks in unstructured environments can be accompished, which reduces greatly the cost and steup time for the robotic system to fit to he well-defined and structured working environments. This paper proposes a dynamic control scheme for robot manipulator with eye-in-hand camera configuration. To perfom the tasks defined in the image plane, the camera motion Jacobian (image Jacobian) matrix is used to transform the camera motion to the objection position change. In addition, the dynamic learning controller is designed to improve the tracking performance of robotic system. the proposed control scheme is implemented for tasks of tracking moving objects and shown to outperform the conventional visual servo system in convergence and robustness to parameter uncertainty, disturbances, low sampling rate, etc.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• International Journal of Precision Engineering and Manufacturing
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• v.5 no.1
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• pp.27-35
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• 2004

In the field of machine vision using a single camera mounted on a mobile robot, although the detection and tracking of moving objects from a moving observer, is complex and computationally demanding task. In this paper, we propose a new scheme for a mobile robot to track and capture a moving object using images of a camera. The system consists of the following modules: data acquisition, feature extraction and visual tracking, and trajectory generation. And a single camera is used as visual sensors to capture image sequences of a moving object. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.25-30
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• 2012

It is important to detect dangerous objects which are intentionally abandoned in public places. Nowadays visual surveillance system is required to enhance the performance in two ways : high resolution and wireless linking ability. In this study the design of visual surveillance system is newly proposed to detect abandoned objects for social security purpose based on wireless high resolution image transmission technology. Also, to enhance PED, PAT performance, the tracking algorithm is included in the previous visual surveillance software scheme. By implementing proposed design scheme on the real wireless high resolution image transmission system, the effectiveness of the overall system is shown with the transmission performance of 4.0 Gbps speed.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.515-526
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• 2003

Tutorial contents of kinematics and dynamics of a wheeled drive mobile robot are presented. Based on the dynamic model, simulation studies of position tracking of a mobile robot are performed. The control structure of several position control algorithms using visual feedback are proposed and their performances are compared. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position control schemes are proposed. Experiments are conducted and the results show the performance of the vision based neural network control scheme fumed out to be the best among several proposed schemes.

• Journal of information and communication convergence engineering
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• v.3 no.1
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• pp.56-61
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• 2005

In this paper, we present a video rate control scheme for storage and delivery in which the time-varying viewing interests are controlled by human gaze. To track the gaze, the pupil's movement is detected using the three-step process : detecting face region, eye region, and pupil point. To control bit rates, the quantization parameter (QP) is changed by considering the static parameters, the video object priority derived from the pupil tracking, the target PSNR, and the weighted distortion value of the coder. As results, we achieved human interfaced visual model and corresponding region-of-interest rate control system.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.