Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155050,
        author = {Nilesh B. Kharade and Mandlik S.Y},
        title = {Human Pose Estimation for Virtual Trainers},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1198-1200},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155050},
        abstract = {Human pose estimation localizes body key points to accurately recognize the postures of people given a picture. This step may be a crucial prerequisite to multiple tasks of computer vision which include human activity recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. A deep structure that might represent a man’s body in several models will help in improved recognition of body parts and also the spatial correlation between them. For hand detection, features supported by hand shape and representation of geometrical details are derived with the assistance of hand contour. An adaptive and unsupervised approach supported region is primarily used for the color image segmentation problem. This process includes the identification of key points of the body, which can include body joints and parts. The identification parts are tough thanks to small joints.We propose a system which will hep Detect human pose .},
        keywords = {Pose Estimation, Machine learning, Key points},
        month = {},
        }