Remarks on 3D Human Body Posture Estimation System Using Sinple Multi-Camera System

2006 ◽  
Author(s):  
Kazuhiko Takahashi ◽  
Yusuke Nagasawa ◽  
Masafumi Hashimoto
Keyword(s):  
Human Body ◽  
Body Posture ◽  
Camera System ◽  
Posture Estimation ◽  
Estimation System

scholarly journals Real-Time Human Body Posture Estimation Using Neural Networks.

2001 ◽  
Vol 44 (3) ◽  
pp. 618-625 ◽  
Author(s):  
Kazuhiko TAKAHASHI ◽  
Tetsuya UEMURA
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Human Body ◽  
Body Posture ◽  
Posture Estimation

scholarly journals Human Body 3D Posture Estimation Using Significant Points and Two Cameras

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chia-Feng Juang ◽  
Teng-Chang Chen ◽  
Wei-Chin Du
Keyword(s):  
Human Body ◽  
Color Space ◽  
Three Dimensional ◽  
The Body ◽  
Support Vector ◽  
Segmentation Method ◽  
Current Frame ◽  
Depth Sensors ◽  
Posture Estimation ◽  
Estimation System

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.

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