• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.265-274
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• 2012

In this paper, we propose a method for preceding vehicle detection and tracking with motion estimation by radar-vision sensor fusion. The motion estimation proposed results in not only correction of inaccurate lateral position error observed on a radar target, but also adaptive detection and tracking of a preceding vehicle by compensating the changes in the geometric relation between the ego-vehicle and the ground due to the driving. Furthermore, the feature-based motion estimation employed to lessen computational burden reduces the number of deployment of the vehicle validation procedure. Experimental results prove that the correction by the proposed motion estimation improves the performance of the vehicle detection and makes the tracking accurate with high temporal consistency under various road conditions.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.157-164
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• 2015

The existing input device is limited to keyboard and mouse. However, recently new type of input device has been developed in response to requests from users. To reflect this trend we propose the new type of input device that gives instruction as analyzing the hand motion of image without special device. After binarizing the skin color area using Cam-Shift method and tracking, it recognizes the hand motion by inputting the finger areas and the angles from the palm center point, which are separated through labeling, into four cardinal directions and counting them. In cases when specific background was not set and without gloves, the recognition rate remained approximately at 75 percent. However, when specific background was set and the person wore red gloves, the recognition rate increased to 90.2 percent due to reduction in noise.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.77-85
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• 2018

This paper proposes a robust digital video stabilization algorithm to extract and track an object, which uses a phase correlation-based motion correction. The proposed video stabilization algorithm consists of background stabilization based on motion estimation and extraction of a moving object. The motion vectors can be estimated by calculating the phase correlation of a series of frames in the eight sub-images, which are located in the corner of the video. The global motion vector can be estimated and the image can be compensated by using the multiple local motions of sub-images. Through the calculations of the phase correlation, the motion of the background can be subtracted from the former frame and the compensated frame, which share the same background. The moving objects in the video can also be extracted. In this paper, calculating the phase correlation to track the robust motion vectors results in the compensation of vibrations, such as movement, rotation, expansion and the downsize of videos from all directions of the sub-images. Experimental results show that the proposed digital image stabilization algorithm can provide continuously stabilized videos and tracking object movements.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.995-1006
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• 1989

If robots have the ability to track the parts on a moving conveyor belt, the efficiency of the manipulation tasks will be increased. This paper presents a motion planning algorithm for conveyor tracking. Tracking trajectory of a robot manipulator is determined by belt speed, initial part position, and initial robot position. Torque limit, maximum velocity, maximum acceleration and maximum jerk are also taken into account. To obtain the tracking solution, the problem is converted to the linear quadratic tracking problem. We describe the manipulator dynamics as second order state equation using parametric functions. Constraints on torques and smoothness are converted to those on input and state variables. The solution of the state equation which minimizes the performance index is obtained by dynamic programming method. Numerical examples are then presented to demonstrate the utility of the motion planning method developed.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.400-405
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• 2006

In this paper, we propose a moving object tracking algorithm by using motion estimation in UAV(Unmanned Aerial Vehicle) video. Proposed algorithm is based on generation of initial image from detected reference image, and tracking of moving object under the time-varying image. With a series of this procedure, tracking process is stable even when the UAV camera sways by correcting position of moving object, and tracking time is relatively reduced. A block matching algorithm is also utilized to determine the similarity between reference image and moving object. An experimental result shows that our proposed algorithm is better than the existing full search algorithm.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1203-1204
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• 2008

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Basing on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, 3D tracking of the target object can be possible.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.799-804
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• 2003

Friction phenomenon can be described as two parts which are pre-sliding and sliding regions. In motion of the sliding region, friction forces depend on the velocity of the system and are known as Coulomb, stick-slip, stribeck effect and viscous friction. The pre-sliding region, which is before breakaway, depends on the position of the system. The motion of friction in the sliding region can be described as the LuGre model. But the pre-sliding motion of friction, which has hysteresis characteristics in general, is not known widely. Therefore, an improved friction model, which can describe the motion of friction in the pre-sliding region, is proposed in this paper. And simulation and experimental results show the effectiveness of the proposed friction model for precise tracking control systems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.769-776
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• 2007

This paper proposes the human arm motion tracking algorithm based on the signal processing for surface EMG (electromyogram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the flexion and extension motions of elbow and shoulder. Also, we show the validity of the suggested algorithms through the experiments.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.774-779
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• 2016

The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.