HUMAN POSE ESTIMATION SYSTEM USING DEEP LEARNING ALGORITHMS

Abstract

The purpose of this work is the software implementation of neural network that can solve problem of Human Pose Estimation. With rapid improvements of neural network models and computing resources over last 10 years it’s become possible to automate a lot of processes, carry out research and improve quality of life. One of the directions is Computer Vision: it allows to recognize objects, track motions, image segmentation, facial recognition etc. Human pose estimation is the part of Computer Vision area of research. It allows to capture human pose from a video or an image and have many uses in medicine, sport, augmented reality, video games etc. Therefore, the goal of this work is to find and optimize algorithm, that is relatively accurate, for identifying and classifying the joints in the human body. To achieve the goal, the following tasks were solved: current methods and technologies that is commonly used to solve problem of human pose estimation were reviewed and analyzed, artificial neural networks were used as a mathematical apparatus for the model, software implementation for human pose estimation was developed and tested, outputs from model were analyzed and evaluated, results and conclusion were formulated.