Abstract
The development of 3D video in recent years realizes 3D surface capturing of human in motion as is. In this enote, we introduce 3D human sensing algorithms based on 3D video. Since 3D video capturing does not require the object to attach special markers, we can capture the original information such as body motion or viewing directions without any disturbance caused by the sensing system itself.
INTRODUCTION:
The development of 3D video technology in recent years has realized 3D shape capturing of the object in motion as is . Since 3D video is captured by conventional 2D cameras, the object is not required to attach special markers nor wear a special costume. This is a clear advantage against other motion capturing technologies, and therefore 3D video is suitable for 3D digital archiving of human motion including intangible cultural assets. However, 3D video itself is merely a non-structured 3D surface data as same as pixel streams of conventional 2D video. In this paper we show how we can sense the human activity from raw 3D video
3D Video
The term “3D video” or “free viewpoint video” includes two different approaches in literature. One approach is called “model-based” methods which reconstruct 3D shapes of the object first and then render them as same as CG. The other approach is “image-based” methods which interpolate a 2D image at a virtual camera position directly from 2D multi-viewpoint images. For 3D human sensing, modelbased approaches are suitable since image-based methods do not produce 3D information. The 3D shape estimation done in the model-based approaches is a classic but open problem in computer vision.
CONCLUSION:
We introduced human activity sensing algorithms from 3D video. algorithms cover
(1) global kinematic structure,
(2) complex motion estimation, and
(3) detailed face and eye direction estimation.
These are all non-contact sensing and do not require the object to use neither a special marker nor a costume. This is a clear advantage of our 3D video based sensing.
