Human Pose Estimation using Deep Learning Techniques
Anant Grover, Deepak Arora, Anuj Grover · 2022
Human Pose Estimation is a method of detecting and classifying all the key human body joints. A 2D Human Pose Estimation method predicts the two-dimensional coordinates from an input image. Classical methods used deformable parts models for detecting body joints. This paper discusses the state-of-the-art Deep Learning based approaches for estimating human poses. It can be implemented by either using the top-down or bottom-up technique for pose estimation. Here, the authors have studied the Convolutional Pose Machines, Hourglass Network and MoveNet architectures for Pose Estimation. These architectures are compared against each other in terms of their efficiency by measuring the time taken by each of them and their accuracy by evaluating them using the Percentage of Detected Joints metric on the MPII dataset. The architectures were implemented by using TensorFlow and Open-Source libraries built using PyTorch. The inference results for each joint were compared individually for all the body joints. The Hourglass Network showed the best accuracy but is really slow. The MoveNet architecture is the second best according to accuracy but is very fast and can be used in real-time systems.