In the domain of computer vision, human pose estimation is becoming increasingly significant. It's one of the most compelling areas of research, and it's gaining a lot of interest due to its usefulness and flexibility in a variety of fields, including healthcare, gaming, augmented reality, virtual trainings and sports. Human pose estimation has opened a door of opportunities. This paper proposes a model for estimation and classification of karate poses which can be used in virtual karate posture correction and trainings. A pretrained model, PoseNet has been used for pose estimation using the results of which the angles between specific joints are calculated and fed into a K-Nearest Neighbors Classifier to classify the poses. The results obtained show that the model achieves an accuracy of 98.75%.
@article{aju2022,
author = {Abin Aju and Christa Mathew and O. S. Gnana Prakasi},
title = {{PoseNet based Model for Estimation of Karate Poses}},
journal = {Journal of Innovative Image Processing},
volume = {4},
number = {1},
pages = {16-25},
year = {2022},
publisher = {IRO Journals},
doi = {10.36548/jiip.2022.1.002},
url = {https://doi.org/10.36548/jiip.2022.1.002}
}
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