This code was built on top of Stacked-Hour-Glass.
Human Pose Estimation (HPE) seeks to find human body components and construct human body representations (e.g., skeletons) from input data such as photographs and videos. It has gained popularity over the last decade and has been used in various applications such as human-computer interaction, entertainment, and virtual reality. Although recently developed deep learning-based solutions have achieved high performance in human pose estimation, challenges remain due to a lack of training data, depth ambiguities, and occlusion. In this paper, we represent our modification of Stacked Hourglass Networks [5], which significantly increases performance by 13% regarding the model speed with an increase in accuracy by around 0.7%