Review on Human Pose Estimation using AI Fitness Tracker

Abstract

Human pose estimation, a fundamental computer vision task, has witnessed significant advancements with the advent of deep learning techniques. This paper provides an overview of recent developments in human pose estimation, focusing on the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We explore the distinctions between 2D and 3D pose estimation, highlighting the challenges and solutions for each. The paper discusses the various techniques for single-person and multi-person pose estimation, as well as the trade-offs between part-based and holistic approaches. Additionally, we delve into the real-time and offline aspects of pose estimation and the significance of temporal analysis in videos. Lastly, we touch on the translation of 2D pose information into 3D space. This review underscores the practical implications of human pose estimation in diverse fields such as sports analysis, healthcare, surveillance, and entertainment. The dynamic nature of this field, fueled by ongoing research and technological advancements, promises to unlock new possibilities for human-computer interaction, augmented reality, and beyond. This abstract provides an overview of the paper's focus on deep learning in the context of human pose estimation and highlights the key aspects of the field. You can further tailor it to your specific paper or presentation needs. Keywords: Human pose estimation, camera, Artificial intelligence, Machine learning