• The KIPS Transactions:PartB
• /
• v.18B no.1
• /
• pp.11-20
• /
• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.359-361
• /
• 2012

본 논문에서는 깊이영상에서 상반신 포즈 분석을 위하여 원통좌표시스템을 제안한다. 깊이영상에서 포즈 후보 영역을 설정하고, 포즈 후보 영역을 이용하여 카메라로부터 신체 중심점까지의 거리와 신체 특징에 따라 원통좌표계를 설정한다. 그리고 밝기값으로 표현되는 깊이 정보를 이용하여 특징벡터를 추출한다. 추출된 원통좌표계의 특징벡터는 원형의 특징공간에 표현되고 포즈 패턴으로 분류된다. 그리고 포즈 패턴들은 특징벡터들의 평균값을 이용하여 학습되고 미리 정의된 포즈 패턴들과 유클리디언 거리로 비교하여 포즈로 분류된다. 본 논문은 상반신 포즈 후보 영역에 동적인 원통 모델을 적용하여 간단한 연산을 통해 머리와 몸통, 팔을 구분할 수 있도록 효과적인 포즈 정보 추출에 목적을 두고 있다.

• 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.

• 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.

• 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.