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Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images

M. Dantone, J. Gall, C. Leistner, and L. Van Gool.
IEEE Transactions on Pattern Analysis and Machine Intelligence
2014, in press


In this work, we address the problem of estimating 2d human pose from still images. Articulated body pose estimation is challenging due to the large variation in body poses and appearances of the different body parts. Recent methods that rely on the pictorial structure framework have shown to be very successful in solving this task. They model the body part appearances using discriminatively trained, independent part templates and the spatial relations of the body parts using a tree model. Within such a framework, we address the problem of obtaining better part templates which are able to handle a very high variation in appearance. To this end, we introduce parts dependent body joint regressors which are random forests that operate over two layers. While the first layer acts as an 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 helps to overcome typical ambiguities of tree structures, such as self-similarities of legs and arms. In addition, we introduce a novel dataset termed F ashionP ose that contains over 7;000 images with a challenging variation of body part appearances due to a large variation of dressing styles. In the experiments, we demonstrate that the proposed parts dependent joint regressors outperform independent classifiers or regressors. The method also performs better or similar to the state-of-the-art in terms of accuracy, while running with a couple of frames per second.

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  author = {M. Dantone and J. Gall and C. Leistner and and L. Van Gool.},
  title = {Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images},
  journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2014},
  month = {},
  pages = {},
  volume = {},
  number = {},
  keywords = {},
  note = {in press}