An Efficient Deep Convolutional Neural Network Model For Yoga Pose
  Recognition Using Single Images
Santosh Kumar Yadav Apurv Shukla Kamlesh Tiwari Hari Mohan Pandey Shaik Ali Akbar
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.