Top-Down Approaches in Human Pose Estimation: A State-of-the Art Review

Abstract

This paper offers a comprehensive exploration of top-down approaches in human pose estimation, a key facet of computer vision. These approaches primarily focus on identifying the human subject in an image or video, followed by determining the spatial configuration of their body joints. Such techniques are instrumental in an array of sectors, from healthcare and sports analytics to entertainment and security systems. The document delves into the foundations of top-down pose estimation, presenting a review of established and emerging models. It explicates the role of key performance metrics, including Average Precision (AP), AP at specific Intersection over Union (IoU) thresholds (AP50, AP75), Average Recall (AR), and AR at an IoU of 0.50, in appraising the efficiency and reliability of these models. The paper underscores the substantial strides made in top-down pose estimation and discusses their efficacy in managing diverse real-world scenarios. It draws attention to the various challenges associated with these techniques, such as handling occlusions, processing images or videos with multiple individuals, and addressing computational constraints. In conclusion, while top-down approaches in pose estimation have shown notable progress and promise, there exist avenues for further research and development. This paper intends to provide a foundational understanding of these techniques and a platform for future advancements in the field.