Accurate human pose estimation plays an important role in various applications such as sports analysis, health care and gaming. Even though recent approaches have shown that 3D positions of body joints can be estimated from a single depth sensor, the depth data often suffer from sensing noise and self-occlusion. In this paper, we present a novel pipeline to estimate the pose of a human body by using two depth sensors. The two sensors simultaneously capture the front and back of the body's movement. Using a wide-baseline RGB-D camera calibration algorithm, the two 3D scans are first geometrically aligned, and then registered to a generic human template using a Gaussian-mixture-model based point set registration procedure with local structure constraints. The new pose of person is finally estimated by a rigid bone-based pose transformation. Experimental results demonstrate the effectiveness of our system in estimating the body pose over other state-of-the-arts techniques.
|Title of host publication||2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings|
|Number of pages||5|
|State||Published - Aug 3 2016|
|Event||23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States|
Duration: Sep 25 2016 → Sep 28 2016
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||23rd IEEE International Conference on Image Processing, ICIP 2016|
|Period||9/25/16 → 9/28/16|
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation under Grant 1237134.
© 2016 IEEE.
- Human pose estimation
- Non-rigid registration
- RGB-D cameras
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing