Human pose estimation using two RGB-D sensors
Wanxin Xu, Po-Chang Su, Sen-ching S. Cheung · 2016
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.