Region-based Network for Yoga Pose Estimation with Discriminative Fine-Tuning Optimization


  • Shilpa Gite Symbiosis Institute of Technology, SIT, Pune, Maharashtra, 412115
  • Deepak T. Mane Vishwakarma Institute of Technology, SIT, Pune, Maharashtra, 412115
  • Vijay Mane Vishwakarma Institute of Technology, SIT, Pune, Maharashtra, 412115
  • Sunil Kale Vishwakarma Institute of Information Technology, Pune-411048, Maharashtra, India
  • Prashant Dhotre MIT Art, Design and Technology University, Pune- 412201,Maharashtra, India


Yoga pose estimation, region-based network, CNN RCNN deep-learning, optimization


Pose estimation of human activity recognition has been a keen area of interest in augmented reality experiences, gaming and robotics, animations, behavioral analysis, and more. One such exciting variant of pose estimation in the field of health and science is yoga pose estimation. This paper explores yoga pose estimation using deep learning networks. The research aims to build a system for estimating   45 different complex yoga asanas from 11,000 images using deep learning algorithms. This system is built using a Region-based Convolutional Neural Network (RCNN) to estimate the joints in the body, followed by a Convolutional Neural Network (CNN) for classifying the poses. The model is trained using the Yoga-82 (hierarchically labeled) dataset, a new dataset with complex pose variations mainly designed for hierarchical labeling. Next, it highlights the pose estimation task through ResNet models followed by an optimization algorithm, which increases the accuracy by 10%. The resultant accuracy is 90.5% for the ResNet50 model. Finally, it provides a solution for overlapping yoga poses, multi-person, in-air, and non-conventional poses using a dense network of 17 critical points for analysis and prediction.


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How to Cite

Gite, S. ., Mane, D. T. ., Mane, V. ., Kale, S., & Dhotre, P. . (2023). Region-based Network for Yoga Pose Estimation with Discriminative Fine-Tuning Optimization. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 166–184. Retrieved from



Research Article