Human pose estimation is a well-known problem in computer vision to locate
joint positions. Existing datasets for the learning of poses are observed to be
not challenging enough in terms of pose diversity, object occlusion, and
viewpoints. This makes the pose annotation process relatively simple and
restricts the application of the models that have been trained on them. To
handle more variety in human poses, we propose the concept of fine-grained
hierarchical pose classification, in which we formulate the pose estimation as
a classification task, and propose a dataset, Yoga-82, for large-scale yoga
pose recognition with 82 classes. Yoga-82 consists of complex poses where fine
annotations may not be possible. To resolve this, we provide hierarchical
labels for yoga poses based on the body configuration of the pose. The dataset
contains a three-level hierarchy including body positions, variations in body
positions, and the actual pose names. We present the classification accuracy of
the state-of-the-art convolutional neural network architectures on Yoga-82. We
also present several hierarchical variants of DenseNet in order to utilize the
hierarchical labels.

本文提出了细粒度分层姿势分类的概念，并提出了一个大规模的瑜伽姿势识别数据集 Yoga-82，包含三个级别的层次结构，以及基于卷积神经网络的多种分类方法。