• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.3
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• pp.1-7
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• 2010

The aim of this research is to evaluate movement and path characteristics of developed heavy duty handling robot using laser tracker(API T3) according to the ISO 9283 robot performance evaluation criteria. As carry out 3D modeling and simulation using CATIA, a test cube was set up to select moving and measuring range of robot. Performance test for pose and distance accuracy, path and path velocity accuracy under payload zero and 440kgf was accomplished. The resulted output data show the reliability of the developed robot.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.71-74
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• 2022

본 논문은 최적화알고리즘을 이용한 관절각 기반 3차원 자세 추정 기법의 정확도를 휴머노이드 로봇을 이용하여 검증하는 방법을 제안한다. 구글의 자세 추정 오픈소스 패키지인 MPP(MediaPipe Pose)로 특정자세를 취한 휴머노이드 로봇의 관절 좌표를 카메라의 픽셀 좌표로 추출한다. 추출한 픽셀 좌표를 전역최적화 방법인 uDEAS(univariate Dynamic Encoding Algorithm for Searches)를 통해 시상면과 관상면에서의 각도를 추정하고 휴머노이드 로봇의 실제 관절 각도와 비교하여 알고리즘의 정확도를 검증하는 방법을 제시한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.25-33
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• 2010

In this paper, we propose a simple and efficient algorithm for head pose estimation using a single camera. First, four subimages are obtained from the camera image for face feature extraction. These subimages are used as feature templates. The templates are then tracked by Kalman filtering, and camera projective matrix is computed by the projective mapping between the templates and their coordinate in the 3D coordinate system. And the user's face pose is estimated from the projective mapping between the user's face and image plane. The accuracy and the robustness of our technique is verified on the experimental results of several real video sequences.

• 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.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.499-506
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• 2006

This Paper describes a fast and stable camera pose estimation method for real-time augmented reality systems. From the feature tracking results of a marker on a single frame, we estimate the camera rotation matrix and the translation vector. For the camera pose estimation, we use the shape factorization method based on the scaled orthographic Projection model. In the scaled orthographic factorization method, all feature points of an object are assumed roughly at the same distance from the camera, which means the selected reference point and the object shape affect the accuracy of the estimation. This paper proposes a flexible and stable selection method for the reference point. Based on the proposed method, we implemented a video augmentation system that inserts virtual 3D objects into the input video frames. Experimental results showed that the proposed camera pose estimation method is fast and robust relative to the previous methods and it is applicable to various augmented reality applications.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.613-617
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• 1990

This paper presents a method of calculating the position and orientation of a polyhedron arbitrarily placed in 3-D space using two cameras. We use key feature of the object and CAD data to solve the correspondence problem between two cameras' images.

• Journal of the Korea Computer Graphics Society
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• v.13 no.2
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• pp.17-22
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• 2007

In early stages of film or animation production, storyboard is used to visually describe the outline of a story. Drawings or photographs, as well as the texts, are employed for character / item placements and camera pose. However, commercially available storyboard tools are mainly drawing and editing tools, not providing functionality for item placement and camera control. In this paper, an Augmented Reality based storyboard tool is presented, which provides an intuitive and easy-to-use interface for storyboard development. Using the presented tool, non-expert users may compose 30 scenes in his or her real environments through tangible building blocks which are used to fetch corresponding 3D models and their pose.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.66-68
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• 2018

본 논문에서는 비디오로부터 추출된 프레임에서 3D 인체 모델의 복원하고 이를 부드럽게 재생될 수 있도록 보정하는 기법을 제안한다. 매개변수 기반의 모델을 사용하여 자세 및 체형을 복원하도록 접근하고 있다. 매개변수 기반의 인체 모델은 다양한 인체 데이터의 학습을 통해 만들어지며 입력 영상으로부터 최적의 자세와 체형 매개변수 값을 찾아 복원하게 된다. 자세 복원은 CNN 을 사용하여 영상으로부터 인체의 관절 위치를 추정하고 3D 모델로부터 2D 로 투영을 통해 관절 간의 거리가 최소화되는 매개변수 값을 찾아 복원한다. 형상 복원은 2D 영상으로부터 취득된 사람의 윤곽 데이터와 3D 모델의 윤곽 데이터 간의 매칭을 통해 복원된다. 이러한 단일 입력 영상에서 비디오와 같은 다중 입력 영상으로 확장하여 칼만 필터를 적용하여 오류 프레임을 검출하고 이전, 이후 프레임의 매개변수와의 보간을 통해 보다 자연스럽고 정확한 모델을 생성한다.