Abstract
Human Pose Estimation (HPE) is a Computer Vision problem that has become increasingly popular over the last few years, with multiple applications in the medical field such as therapy using virtual and augmented reality, robot caregivers, virtual physical therapy and kinematic analysis. Nevertheless, all the machine learning algorithms developed for these applications are trained in small datasets with images captured in constrained scenarios and with information given by sensors, bounding the applicability of these methods. We developed a simple yet useful deep learning algorithm for Human Pose Estimation that uses as input only an image of a scene with people. The estimated position of the joints and body parts can be used to retrieve basic kinematic information from the people on the image that can be applied to the aforementioned medical applications. We focus on overcoming the limit of Human Pose Estimation algorithms due to jittering, aiming to preserve more precise pixel location. Thus, we explore different novel approaches to improve the precision of the existing state- of-the-art algorithms in keypoint estimation and evaluate them on COCO keypoint dataset, outperforming the current top methods. We hope our algorithm encourages the academic community to develop simpler but precise HPE algorithms for medical applications based on RGB images.