Under the assumption that no dynamic prior is given, the main obstacle to practical human-motion tracking is the high number of dimensions associated with a 3-D articulated full-body model. We present a method for 3-D human-motion tracking when the training data are unavailable in advance and no motion pattern prior is assumed. Based on the framework of the annealed particle filter, the proposed algorithm incrementally learns the eigenspace as a compact representation of motion patterns and efficiently adapts to pose changes. The model updates using the principal-component analysis, and introduces a forgetting factor to avoid overfitting. In addition, the likelihood measure is modeled by minimizing a cost function on the 3-D Markov random field (MRF), which integrates the information from visual hull and shape priors. A dynamic graph cut is performed to speed up the minimization process. As a result, the proposed approach is capable of obtaining the pose in parallel with the voxel data. Experimental results suggest that our method performs online tracking robustly and generates reconstructions beyond the shape from silhouette method from sparse camera views.
We present a two-stage scheme integrating voxel reconstruction and human motion tacking. By combining voxel
reconstruction with human motion tracking interactively, our method can work in a cluttered background where perfect
foreground silhouettes are hardly available. For each frame, a silhouette-based 3D volume reconstruction method and
hierarchical tracking algorithm are applied in two stages. In the first stage, coarse reconstruction and tracking results are
obtained, and then the refinement for reconstruction is applied in the second stage. The experimental results demonstrate
our approach is promising. Although our method focuses on the problem of human body voxel reconstruction and
motion tracking in this paper, our scheme can be used to reconstruct voxel data and infer the pose of many specified rigid
and articulated objects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.