• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.11-19
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• 2016

The size of a display is large, The form becoming various of that do not apply to previous methods of gaze tracking and if setup gaze-track-camera above display, can solve the problem of size or height of display. However, This method can not use of infrared illumination information of reflected cornea using previous methods. In this paper, Robust pupil detecting method for eye's occlusion, corner point of inner eye and center of pupil, and using the face pose information proposes a method for calculating the simply position of the gaze. In the proposed method, capture the frame for gaze tracking that according to position of person transform camera mode of wide or narrow angle. If detect the face exist in field of view(FOV) in wide mode of camera, transform narrow mode of camera calculating position of face. The frame captured in narrow mode of camera include gaze direction information of person in long distance. The method for calculating the gaze direction consist of face pose estimation and gaze direction calculating step. Face pose estimation is estimated by mapping between feature point of detected face and 3D model. To calculate gaze direction the first, perform ellipse detect using splitting from iris edge information of pupil and if occlusion of pupil, estimate position of pupil with deformable template. Then using center of pupil and corner point of inner eye, face pose information calculate gaze position at display. In the experiment, proposed gaze tracking algorithm in this paper solve the constraints that form of a display, to calculate effectively gaze direction of person in the long distance using single camera, demonstrate in experiments by distance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.31-40
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• 2003

Most face recognition systems are based on 2D images and applied in many applications. However, it is difficult to recognize a face when the pose varies severely. Therefore, head pose estimation is an inevitable procedure to improve recognition rate when a face is not frontal. In this paper, we propose a novel head pose estimation algorithm for 3D face recognition. Given the 3D range image of an unknown face as an input, we automatically extract facial feature points based on the face curvature. We propose an Error Compensated Singular Value Decomposition (EC-SVD) method based on the extracted facial feature points. We obtain the initial rotation angle based on the SVD method, and perform a refinement procedure to compensate for remained errors. The proposed algorithm is performed by exploiting the extracted facial features in the normaized 3D face space. In addition, we propose a 3D nearest neighbor classifier in order to select face candidates for 3D face recognition. From simulation results, we proved the efficiency and validity of the proposed algorithm.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.600-607
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• 2008

This paper proposes a robust human detection method irrespective of their pose variation using the multiple oriented 2D elliptical filters (MO2DEFs). The MO2DEFs can detect the humans regardless of their poses unlike existing object oriented scale adaptive filter (OOSAF). To overcome OOSAF's limitation, we introduce the MO2DEFs whose shapes look like the oriented ellipses. We perform human detection by applying four different 2D elliptical filters with specific orientations to the 2D spatial-depth histogram and then by taking the thresholds over the filtered histograms. In addition, we determine the human pose by using convolution results which are computed by using the MO2DEFs. We verify the human candidates by either detecting the face or matching head-shoulder shapes over the estimated rotation. The experimental results showed that the accuracy of pose angle estimation was about 88%, the human detection using the MO2DEFs outperformed that of using the OOSAF by $15{\sim}20%$ especially in case of the posed human.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.240-251
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• 2016

This paper presents a real-time indoor location and pose estimation method that utilizes simple artificial markers and image analysis techniques for the purpose of warehouse automation. The conventional indoor localization methods cannot work robustly in warehouses where severe environmental changes usually occur due to the movement of stocked goods. To overcome this problem, the proposed framework places artificial markers having different interior pattern on the predefined position of the warehouse floor. The proposed algorithm obtains marker candidate regions from a captured image by a simple binarization and labeling procedure. Then it extracts maker interior pattern information from each candidate region in order to decide whether the candidate region is a true marker or not. The extracted interior pattern information and the outer boundary of the marker are used to estimate location and heading angle of the localization system. Experimental results show that the proposed localization method can provide high performance which is almost equivalent to that of the conventional method using an expensive LIDAR sensor and AMCL algorithm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.2
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• pp.223-229
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• 2017

Recently, robotic bin-picking tasks have drawn considerable attention, because flexibility is required in robotic assembly tasks. Generally, stereo camera systems have been used widely for robotic bin-picking, but these have two limitations: First, computational burden for solving correspondence problem on stereo images increases calculation time. Second, errors in image processing and camera calibration reduce accuracy. Moreover, the errors in robot kinematic parameters directly affect robot gripping. In this paper, we propose a method of correcting the bin-picking error by using trinocular vision system which consists of two stereo cameras andone hand-eye camera. First, the two stereo cameras, with wide viewing angle, measure object's pose roughly. Then, the 3rd hand-eye camera approaches the object, and corrects the previous measurement of the stereo camera system. Experimental results show usefulness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.160.3-160
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• 2001

The objective of this research is to monitor and control the vehicle motion in order to remove out the existing safety risk based upon the human-machine cooperative vehicle control. A new control method is proposed to control the steering wheel of the vehicle to keep the lane. Desired angle of the steering wheel to control the vehicle motion could be calculated based upon vehicle dynamics, current and estimated pose of the vehicle every sample steps. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net, where not only the state variables, but also the corresponding uncertainties were propagated in ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.173.1-173
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• 2001

This paper proposes the methodology in image processing algorithm that estimates the pose of the pipe crawling robot. The pipe crawling robots are usually equipped with a lighting device and a camera on its head for monitoring and inspection purpose. The proposed methodology is using these devices without introducing the extra sensors and is based on the fact that the position and the intensity of the reflected light varies with the robot posture. The algorithm is divided into two parts, estimating the translation and rotation angle of the camera, followed by the actual pose estimation of the robot. To investigate the performance of the algorithm, the algorithm is applied to a sewage maintenance robot.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.257-257
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• 2000

The objective of this research is to monitor and control the vehicle motion in order to remove out the existing safety risk based upon the human-machine cooperative vehicle control. A predictive control method is proposed to control the steering wheel of the vehicle to keep the lane. Desired angle of the steering wheel to control the vehicle motion could be calculated based upon vehicle dynamics, current and estimated pose of the vehicle every sample steps. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net, where not only the state variables, but also the corresponding uncertainties were propagated in forward and backward direction in such a way to satisfy the given constraint condition, maintain consistency, reduce the uncertainties, and guarantee robustness. A series of experiments was conducted to evaluate the control performance, in which a car Like robot was utilized to quit unwanted safety problem. As the results, the robot was keeping very well a given lane with arbitrary shape at moderate speed.

• Smart Media Journal
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• v.1 no.2
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• pp.40-46
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• 2012

In this paper, we describe interactive contents which is used the result of the inputted interface recognizing vision-based body gesture. Because the content uses the imp which is the common culture as the subject in Asia, we can enjoy it with culture familiarity. And also since the player can use their own gesture to fight with the imp in the game, they are naturally absorbed in the game. And the users can choose the multiple endings of the contents in the end of the scenario. In the part of the gesture recognition, KINECT is used to obtain the three-dimensional coordinates of each joint of the limb to capture the static pose of the actions. The vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part Because gestures can be presented through sequential static poses, we recognize the gestures which are configured poses by using HMM In this paper, we describe the interactive content which is used as input interface by using gesture recognition result. So, we can control the contents using only user's gestures naturally. And we intended to improve the immersion and the interest by using the imp who is used real-time interaction with user.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.