Towards Multilevel Human Body Modeling and Tracking in 3D: Investigation in Laplacian Eigenspace (LE) Based Initialization and Kinematically Constrained Gaussian Mixture Modeling (KC-GMM)

Vision-based automatic human body pose estimation has many potential applications and it is also a challenging task. Together, these two factors have made vision-based human body pose estimation an attractive research area with closely related research areas including body pose, hand pose, and head pose estimation. Up to now, these research works however only deal with each task of estimating body pose, hand pose or head pose separately. In this paper, we bring out the issue of multilevel human body pose estimation and focus on model based methods for articulated human body pose estimation using volumetric data (voxel data). Important steps in this kind of methods will be described and several recent techniques will be analyzed and compared. Based on this analysis, we propose a fairly general method combining the discovered properties of LE transformation in [23] and KC-GMM method [3] for automatic initialization and tracking of both body model and hand model using voxel data. We also propose a framework for human body pose estimation at multilevel (i.e. body pose, hand pose, head pose) in an integrated way. The proposed method and framework will be presented along with experimental support and other possible avenues for future work in the area will be discussed.