Human Pose Estimation Using Body Parts Dependent Joint Regressors

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 probl...

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Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 3041 - 3048
Main Authors Dantone, Matthias, Gall, Juergen, Leistner, Christian, Van Gool, Luc
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Estimation
Head
human pose estimation
joint regressor
Joints
Predictive models
random forest
Training
Vegetation
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Summary: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.391