Human Posture Sequence Estimation Using Two Un-calibrated Cameras
Ruicheng Wang, Wee Kheng Leow · 2005
3D Human posture sequence estimation from single or multiple image sequences is essential in many applications. However, 3D posture sequence cannot be accurately estimated from single image sequence due to depth ambiguity or self-occlusion, and camera calibration is often required before estimating 3D posture sequence from multiple image sequences. In this paper, we present an algorithm to accurately estimate 3D human posture sequence from two un-calibrated image sequences. The algorithm combines a modified Nonparametric Belief Propagation (mNBP) method with an improved camera self-calibration method. The previously developed mNBP can estimate posture even under partial self-occlusion, and here it is improved to estimate posture when the human model scale is different from that of body image in image sequences. The improved self-calibration can guarantee to find the optimal rotation and relative scale between two fixed but un-calibrated scaled orthographic cameras, without a nonlinear optimization process. Quantitative and qualitative results of experiments show that the algorithm is able to estimate 3D posture sequence from a pair of un-calibrated image sequences. 1