TY - GEN
T1 - YogaTube
T2 - 2022 International Joint Conference on Neural Networks, IJCNN 2022
AU - Yadav, Santosh Kumar
AU - Singh, Guntaas
AU - Verma, Manisha
AU - Tiwari, Kamlesh
AU - Pandey, Hari Mohan
AU - Akbar, Shaik Ali
AU - Corcoran, Peter
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Yoga can be seen as a set of fitness exercises involving various body postures. Most of the available pose and action recognition datasets are comprised of easy-to-moderate body pose orientations and do not offer much challenge to the learning algorithms in terms of the complexity of pose. In order to observe action recognition from a different perspective, we introduce YogaTube, a new large-scale video benchmark dataset for yoga action recognition. YogaTube aims at covering a wide range of complex yoga postures, which consist of 5484 videos belonging to a taxonomy of 82 classes of yoga asanas. Also, a three-stream architecture has been designed for yoga asanas pose recognition using two modules, feature extraction, and classification. Feature extraction comprises three parallel components. First, pose is estimated using the part affinity fields model to extract meaningful cues from the practitioner. Second, optical flow is used to extract temporal features. Third, raw RGB videos are used for extracting the spatiotemporal features. Finally in the classification module, pose, optical flow, and RGB streams are fused to get the final results of the yoga asanas. To the best of our knowledge, this is the first attempt to establish a video benchmark yoga recognition dataset. The code and dataset will be released soon.
AB - Yoga can be seen as a set of fitness exercises involving various body postures. Most of the available pose and action recognition datasets are comprised of easy-to-moderate body pose orientations and do not offer much challenge to the learning algorithms in terms of the complexity of pose. In order to observe action recognition from a different perspective, we introduce YogaTube, a new large-scale video benchmark dataset for yoga action recognition. YogaTube aims at covering a wide range of complex yoga postures, which consist of 5484 videos belonging to a taxonomy of 82 classes of yoga asanas. Also, a three-stream architecture has been designed for yoga asanas pose recognition using two modules, feature extraction, and classification. Feature extraction comprises three parallel components. First, pose is estimated using the part affinity fields model to extract meaningful cues from the practitioner. Second, optical flow is used to extract temporal features. Third, raw RGB videos are used for extracting the spatiotemporal features. Finally in the classification module, pose, optical flow, and RGB streams are fused to get the final results of the yoga asanas. To the best of our knowledge, this is the first attempt to establish a video benchmark yoga recognition dataset. The code and dataset will be released soon.
KW - Action recognition
KW - Deep Learning
KW - Multi-stream fusion
KW - Yoga
UR - https://www.scopus.com/pages/publications/85140748780
U2 - 10.1109/IJCNN55064.2022.9892122
DO - 10.1109/IJCNN55064.2022.9892122
M3 - Conference Publication
AN - SCOPUS:85140748780
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2022 through 23 July 2022
ER -