PoseWatch: Advancing Real Time Human Pose Tracking and Juxtaposition with Deep Learning

Ashraf, T and Balaji Prabu, BV and Jois Narasipura, OS (2024) PoseWatch: Advancing Real Time Human Pose Tracking and Juxtaposition with Deep Learning. In: 8th International Conference on Computer Vision and Image Processing, CVIP 2023, 3 November 2023through 5 November 2023, Jammu, India., pp. 13-24.

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Abstract

Human pose estimation is the process of continuously monitoring a person�s action and movement to track and monitor the activity of a person or an object. Human pose estimation is usually done by capturing the key points which describe the pose of a person. A guiding practicing framework that enables people to learn and exercise activities like yoga, fitness, dancing, etc., might be built using human posture recognition remotely and accurately without the help of a personal trainer. This work has proposed a framework to detect and recognize various yoga and exercise poses to help the individual practice the same correctly. A popular Blaze-pose model extracts key points from the student end and compares the same with the instructor pose. The extracted key points are fed to the Human Pose Juxtaposition model (HPJT) to compare the student pose with the instructor. The model will assess the correctness of the pose by comparing the extracted key points and give feedback to students if any corrections need to be made. The proposed model is trained with 40+ yoga and exercise poses, and evaluated the model�s performance with the mAP, and the model achieved an accuracy of 99.04. The results proved that any person could use the proposed framework in real-time to practice exercise, yoga, dance, etc. At their respective location without the help of a physical instructor with precision and accuracy, leading to a healthy life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Item Type:	Conference Paper
Publication:	Communications in Computer and Information Science
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to the publisher.
Keywords:	Deep learning, Client-server models; Deep learning; Human pose; Human pose estimations; Human pose tracking; Human posture recognition; Keypoints; Learn+; Pose models; Real- time, Students
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	30 Aug 2024 10:38
Last Modified:	30 Aug 2024 10:38
URI:	http://eprints.iisc.ac.in/id/eprint/86055

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