Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
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DOI：
10.1109/tpami.2014.2318702
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发表时间：
2014-11-01
影响因子：
23.6
通讯作者：
Van Gool, Luc
中科院分区：
文献类型：
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作者：
Dantone, Matthias;Gall, Juergen;Van Gool, Luc
在这项工作中，我们解决的问题，估计二维人体姿势从静止图像。由于不同身体部位的身体姿势和外观的大变化，铰接式身体姿势估计是具有挑战性的。最近的方法，依赖于图形结构框架已被证明是非常成功的解决这一任务。它们使用区分训练的独立部分模板对身体部分外观进行建模，并使用树模型对身体部分的空间关系进行建模。在这样一个框架内，我们解决的问题，获得更好的部分模板，能够处理一个非常高的变化的外观。为此，我们引入了依赖于身体关节的回归变量，这些回归变量是在两个层上操作的随机森林。虽然第一层充当独立的身体部位分类器，但第二层考虑第一层的估计类分布，从而能够通过对各部位的相互依赖性和共现性进行建模来预测关节位置。这有助于克服树结构的典型模糊性，例如腿和臂的自相似性。此外，我们引入了一个新的数据集，称为FashionPose，其中包含超过7; 000个图像，具有挑战性的变化，由于穿衣风格的变化很大的身体部位外观。在实验中，我们证明了所提出的部分相关的联合回归优于独立的分类器或回归。该方法在精度方面也表现得更好或类似于最先进的技术，同时每秒运行几帧。
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 data set termed FashionPose 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.