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@article{166875,
        author = {Sainath Sapa and Dr. K. Santhi Sree},
        title = {CNN MODEL FOR HUMAN POSE ESTIMATION USING PYTORCH AND OPENCV WITH PYTHON},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2265-2279},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166875},
        abstract = {Human Pose estimation, a fundamental task in computer vision, is pivotal for understanding human actions and behaviors from images or videos. It involves detecting and tracking key points representing the human body with high accuracy and precision. This paper introduces the Pose Estimation System project, which addresses the evolving demands of diverse industries and research endeavors. Leveraging deep learning methodologies and state-of-the-art algorithms, including YOLO (You Only Look Once) and RCNN (Region-based Convolutional Neural Networks) models, the project aims to push the boundaries of pose estimation capabilities. By harnessing artificial intelligence and machine learning, it seeks to empower researchers, developers, and practitioners with robust tools and solutions for tackling complex challenges in human-centric computing. Through open collaboration and knowledge sharing, the project aims to democratize access to pose estimation technologies and accelerate progress towards more intelligent and inclusive computing systems. With an unwavering focus on excellence and impact, the Pose Estimation System project stands poised to shape the future of computer vision and human-computer interaction.

},
        keywords = {Pose estimation, Computer vision, Deep learning, YOLO, RCNN, Artificial intelligence, Human-computer interaction},
        month = {July},
        }