Human Pose Estimation Using Deep Learning Models
Firgat MURADLİ, Serap Çakar Kaman, Feyza ÇEREZCİ, Gülüzar Çit · Sakarya University Journal of Science · 2023
In the last decade, there has been a lot of research into recovering 3D human poses from images. The current datasets need to address frequent exposure estimation problems adequately. However, these are shared resources to assess, inform, and contrast various models. Deep learning models are widely utilized and perform at high levels in many branches of research and engineering. So these models are used in this study with the help of the OpenCV and Keras libraries, which are open-source programs. Providing significant improvements in diversity and difficulty, the MPII Human Pose dataset is used to train and test the ResNet50 and VGG16 models. The validation rate of the dataset is shown to evaluate the model's performance.