Evaluating the Robustness of Human Pose Estimation Models: A Comparative Analysis

This research paper conducts a comparative analysis to evaluate the robustness of human pose estimation models. The study scrutinizes advanced computer vision techniques, including Posenet, MediaPipe, VideoPose3D, and BlazePose, assessing their performance and precision in realtime analysis and feedback provision. In an era of rapid advancements in Artificial Intelligence and computer vision, the potential for predictive capabilities in these tasks is poised for exponential growth. This progress is expected to stem from the fusion of image processing, deep learning, and machine learning techniques, unlocking unprecedented levels of accuracy and efficiency in posture estimation. Additionally, the research involves examining various datasets to determine their suitability and relevance for evaluating pose estimation models. Furthermore, an extensive literature review explores relevant research papers and studies within this evolving field, providing a solid foundation for the comparative analysis undertaken in this research endeavour.