Improved Real-time Yoga Pose Estimation with GAN Augmentation

Yoga pose detection is challenging in computer vision due to variations in body postures and environmental conditions. Recent advancements in DL models have demonstrated encouraging achievements in this field. This study integrates Deep learning (DL) and Machine learning (ML) techniques to detect and monitor 20 Yoga postures through the real-time application. DL techniques like OpenPose, PoseNet, and PIFPAF are applied to the image and video dataset to obtain the keypoint features. These features are combined and provided to train various ML classifiers for Yoga posture detection tasks. Integrating AI augmentation technique Generative Adversarial Networks (GANs) plays a crucial role in improving the robustness and accuracy of the models. GANs are employed to generate synthetic data that mimics real-world variations in yoga poses and environments. By generating realistic variations in poses, backgrounds, lighting, and body shapes, GAN helped the models become more resilient to complex poses and diverse environmental conditions, enhancing their generalization capabilities. All the classifiers showed improvement with augmentation, whereas the Random Forest classifier performed the best in all parameters. Further, the model deployed with a webcam feed for estimating the Yoga pose by the yoga practitioner indicating accuracy level.