RECENT ADVANCES IN HUMAN POSE ESTIMATION: DEEP LEARNING APPROACHES AND REAL-TIME APPLICATIONS

This comprehensive article explores recent advances in human pose estimation (HPE), a critical computer vision task with wide-ranging applications. The article traces the evolution from traditional methods to cutting-edge deep learning approaches, highlighting the transformative impact of convolutional neural networks and transformer-based architectures. It examines state-of-the-art models such as RTMPose, HRNet, and DEKR, detailing their innovative features and performance improvements. The review discusses significant progress in multi-person pose estimation, real-time processing, and performance in challenging environments. Applications across diverse fields including sports analytics, healthcare, robotics, and augmented reality are explored, with a focus on edge device implementation and the potential for lightweight, adaptable models. The article also addresses ongoing challenges in the field, such as improving accuracy in complex scenarios, enhancing real-time performance, and navigating privacy and ethical concerns. By synthesizing current research and identifying future directions, this review provides a comprehensive overview of the rapidly evolving landscape of human pose estimation, its practical implications, and the potential for further advancements that could revolutionize human-computer interaction and our understanding of human movement.