Yoga Posture Classification using Computer Vision
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
Yadav, Santosh Singh, Amitojdeep Gupta, & Abhishek Raheja, Jagdish. (2019). Real- time Yoga recognition using deep learning. Available at: https://link.springer.com/article/10.1007/s00521-019-04232-7.
LI, Ang LI, Yi-xiang LI, & Xue-hui. (2017). TensorFlow and keras-based convolutional neural network in CAT image recognition. DEStech Transactions on Computer Science and Engineering. DOI: 10.12783/dtcse/cmsam2017/16428.
A. Lai, B. Reddy, & B. Vlijmen. (2019). Yog.ai: deep learning for yoga. Available at: http://cs230.stanford.edu/projects_winter_2019/reports/15813480.pdf.
Jain, Shrajal, Rustagi, Aditya, Saurav, Sumeet, Saini, Ravi, & Singh, Sanjay. (2020). Three- dimensional CNN-inspired deep learning architecture for Yoga pose recognition in the real-world environment. Neural Computing and Applications. DOI: 10.1007/s00521-020- 05405-5.
Z. Cao, G. Hidalgo Martinez, T. Simon, S. -E. Wei, & Y. A. Sheikh. (2019). OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2019.2929257.
Z. Cao, T. Simon, S. Wei, & Y. Sheikh. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 1302-1310. DOI: 10.1109/CVPR.2017.143.
M. Dantone, J. Gall, C. Leistner and L., & Van Gool. (2013). Human pose estimation using body parts dependent joint regressors. IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp. 3041-3048. DOI: 10.1109/CVPR.2013.391.
A. Kendall, M. Grimes, & R. Cipolla. (2015). PoseNet: A convolutional network for real- time 6-DOF camera relocalization. IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 2938-2946. DOI: 10.1109/ICCV.2015.336.
S. Kreiss, L. Bertoni, & A. Alahi. (2019). PifPaf: Composite fields for human pose estimation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 11969-11978. DOI: 10.1109/CVPR.2019.01225.
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