Pose recognition deals with designing algorithms to locate human body joints
in a 2D/3D space and run inference on the estimated joint locations for
predicting the poses. Yoga poses consist of some very complex postures. It
imposes various challenges on the computer vision algorithms like occlusion,
inter-class similarity, intra-class variability, viewpoint complexity, etc.
This paper presents YPose, an efficient deep convolutional neural network (CNN)
model to recognize yoga asanas from RGB images. The proposed model consists of
four steps as follows: (a) first, the region of interest (ROI) is segmented
using segmentation based approaches to extract the ROI from the original
images; (b) second, these refined images are passed to a CNN architecture based
on the backbone of EfficientNets for feature extraction; (c) third, dense
refinement blocks, adapted from the architecture of densely connected networks
are added to learn more diversified features; and (d) fourth, global average
pooling and fully connected layers are applied for the classification of the
multi-level hierarchy of the yoga poses. The proposed model has been tested on
the Yoga-82 dataset. It is a publicly available benchmark dataset for yoga pose
recognition. Experimental results show that the proposed model achieves the
state-of-the-art on this dataset. The proposed model obtained an accuracy of
93.28%, which is an improvement over the earlier state-of-the-art (79.35%) with
a margin of approximately 13.9%. The code will be made publicly available.

YPose 是一个用于从 RGB 图像中识别瑜伽姿势的卷积神经网络模型，采用基于 EfficientNets 骨干的 CNN 架构进行特征提取，并添加了密集连接网络架构中自适应的密集细化块用于学习更多样化的特征，然后应用全局平均池化和全连接层用于瑜伽姿态的多级分类，其在 Yoga-82 数据集上的表现优于之前的最新技术，准确率达到了 93.28%。