• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.619-628
• /
• 2011

Normally 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. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.186-192
• /
• 2009

In this paper, we represent the recognition method for human frontal upper body pose. In HCI(Human Computer Interaction) and HRI(Human Robot Interaction) when a interaction is established the human has usually frontal direction to the robot or computer and use hand gestures then we decide to focus on human frontal upper-body pose, The two main difficulties are firstly human pose is consist of many parts which cause high DOF(Degree Of Freedom) then the modeling of human pose is difficult. Secondly the matching between image features and modeling information is difficult. Then using Pictorial Model we model the human main poses which are mainly took the space of frontal upper-body poses and we recognize the main poses by making main pose database. using determined main pose we used the model parameters for particle filter which predicts the posterior distribution for pose parameters and can determine more specific pose by updating model parameters from the particle having the maximum likelihood. Therefore based on recognizing main poses and tracking the specific pose we recognize the human frontal upper body poses.

• Smart Media Journal
• /
• v.5 no.2
• /
• pp.15-23
• /
• 2016

In this paper we propose the depth-poselets using body-part-poses and also propose the method to recognize the gesture. Since the gestures are composed of sequential poses, in order to recognize a gesture, it should emphasize to obtain the time series pose. Because of distortion and high degree of freedom, it is difficult to recognize pose correctly. So, in this paper we used partial pose for obtaining a feature of the pose correctly without full-body-pose. In this paper, we define the 16 gestures, a depth image using a learning image was generated based on the defined gestures. The depth poselets that were proposed in this paper consists of principal three-dimensional coordinates of the depth image and its depth image of the body part. In the training process after receiving the input defined gesture by using a depth camera in order to train the gesture, the depth poselets were generated by obtaining 3D joint coordinates. And part-gesture HMM were constructed using the depth poselets. In the testing process after receiving the input test image by using a depth camera in order to test, it extracts foreground and extracts the body part of the input image by comparing depth poselets. And we check part gestures for recognizing gesture by using result of applying HMM. We can recognize the gestures efficiently by using HMM, and the recognition rates could be confirmed about 89%.

• The KIPS Transactions:PartB
• /
• v.15B no.5
• /
• pp.435-448
• /
• 2008

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.355-358
• /
• 2012

본 논문은 사용자의 포즈를 인터페이스로 사용하는 가상 비행 체험 콘텐츠에 대하여 기술한다. 사용자의 포즈를 인식하기 위해서 제스처를 구성하는 상반신의 포즈를 식별하여야 한다. 본 논문에서 기술한 콘텐츠는 한정된 공간에서 사용자의 움직임을 인식하고 가상공간에 아바타를 이용하여 표현하고 있다. 그러므로 사용자는 가상공간에서 정의된 포즈를 사용하여 가상 비행을 체험할 수 있고 인식된 포즈는 OS-Value 이벤트를 이용하여 가상 비행 체험 콘텐츠에서 인터페이스로 활용이 가능하다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2017.06a
• /
• pp.99-101
• /
• 2017

최근 딥러닝을 이용한 SAR 영상의 목표물을 인식하는 알고리즘이 괄목할만한 성능을 보여주었다. 이러한 알고리즘들은 포즈 각도 정보를 무시한 채 목표물의 종류를 추정하는 것에만 초점을 맞춘다. 포즈 각도 추정 알고리즘은 단지 SAR 영상 목표물 인식 알고리즘의 전처리 과정으로 연구되었다. 하지만 감시 시스템에서, 목표물이 향하고 있는 방향을 추정하는 것 또한 중요하다. 먼저, 포즈 각도 추정을 통하여 적의 전술 배치를 계획을 추정할 수 있다. 또한 목표물이 아군 쪽을 바라보면 큰 위협이 되는데, 포즈 각도 추정을 통하여 이러한 정보를 알 수 있다. 따라서 본 논문은 목표물이 향하고 방향을 추정할 수 있는 콘볼루션 네트워크를 고안하였다. 네트워크를 학습시키기 위하여 SAR 영상의 목표물의 포즈 각도를 양자화하여 포즈 각도 label 을 구성하였다. 또한 이러한 포즈 각도 추정을 정제하는 알고리즘을 고안하였고 이는 보다 정확한 포즈 각도 추정을 가능하게 하였다. 그 결과, 제안된 네트워크는 포즈 각도 추정에 높은 정확도를 보여준다.

• The Journal of the Korea Contents Association
• /
• v.10 no.8
• /
• pp.11-17
• /
• 2010

In this paper, we propose HMM-based upper-body gesture. First, to recognize gesture of space, division about pose that is composing gesture once should be put priority. In order to divide poses which using interface, we used two IR cameras established on front side and side. So we can divide and acquire in front side pose and side pose about one pose in each IR camera. We divided the acquired IR pose image using SVM's non-linear RBF kernel function. If we use RBF kernel, we can divide misclassification between non-linear classification poses. Like this, sequences of divided poses is recognized by gesture using HMM's state transition matrix. The recognized gesture can apply to existent application to do mapping to OS Value.

• Journal of Internet Computing and Services
• /
• v.5 no.6
• /
• pp.79-91
• /
• 2004

Various facial pose detection and recognition has been a difficult problem. The problem is due to the fact that the distribution of various poses in a feature space is mere dispersed and more complicated than that of frontal faces, This thesis proposes a robust pose-expression-invariant face recognition method in order to overcome insufficiency of the existing face recognition system. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-V-Z axes, Second, the input face is mapped by deformable template using this vectors and 3D CANDIDE face model. Final. the mapped face is transformed to frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses, Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses and expressions.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.63-69
• /
• 2004

This paper proposes a novel method for achieving pose-invariant face recognition using cylindrical model. On the assumption that a face is shaped like that of a cylinder, we estimate the object's pose and then extract the frontal face image via a pose transform with previously estimated pose angle. By employing the proposed pose transform technique we can increase the face recognition performance using the frontal face images. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the pose transform. Additionally, the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.05a
• /
• pp.11-12
• /
• 2008

본 논문은 이차원 영상에 투영된 삼차원 인체의 포즈를 인식하기 위하여 이차원 영상에 투영된 인체의 실루엣 정보를 이용하였다. 인체는 삼차원 공간에서 움직이므로 이차원 영상으로 모든 정보를 알아내기에는 부족한 면이 있다. 따라서 본 논문에서는 인체 포즈의 주시 방향을 결정한 후 인체의 실루엣 영상 Convex-hull 특징점 정보를 이용하여 인체의 삼차원 포즈를 인식하였다. 인체의 포즈는 PCA로 차원을 축소하였으며 Diffusion Distance로 데이터베이스의 포즈모델 중 가장 가까운 모델을 선택하였다.