An Efficient Deep Convolutional Neural Network Model For Yoga Pose Recognition Using Single Images

TL;DR

This paper introduces YPose, an efficient CNN model for recognizing complex yoga poses from images, achieving state-of-the-art accuracy on the Yoga-82 dataset by combining segmentation, EfficientNet-based feature extraction, and dense refinement.

Contribution

The paper proposes a novel deep CNN architecture with segmentation and dense refinement for improved yoga pose recognition from images.

Findings

Achieved 93.28% accuracy on Yoga-82 dataset.

Improved accuracy by approximately 13.9% over previous methods.

Validated effectiveness of combining segmentation with DenseNet-inspired features.

Abstract

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.