Perceiving Systems Conference Paper 2013

Human Pose Estimation using Body Parts Dependent Joint Regressors

Poseregression

In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.

Author(s): Dantone, M. and Gall, J. and Leistner, C. and van Gool, L.
Book Title: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Pages: 3041--3048
Year: 2013
Month: June
Publisher: IEEE
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Address: Portland, OR, USA
DOI: 10.1109/CVPR.2013.391
Electronic Archiving: grant_archive
Attachments:

BibTex

@inproceedings{DGLG13,
  title = {Human Pose Estimation using Body Parts Dependent Joint Regressors},
  booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  abstract = {In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.},
  pages = {3041--3048},
  publisher = {IEEE},
  address = {Portland, OR, USA},
  month = jun,
  year = {2013},
  slug = {dglg13},
  author = {Dantone, M. and Gall, J. and Leistner, C. and van Gool, L.},
  month_numeric = {6}
}