Human Pose Estimation based on HRNet and Feature Pyramids

Abstract

A popular area of research in the realm of computer vision is human pose estimation, which is the process of recovering human joint points from the given images or videos. Convolutional neural networks, which have great feature representation capabilities, have become a fundamental component of human pose estimation algorithms as a result of the deep learning field's quick development. To enhance feature quality and raise the precision of human pose estimate, in this paper, we propose a new model based on HRNet neural network and feature pyramid to more accurately capture each main part of the human body. The model uses HRNet as the backbone network, taking advantage of its ability to combine low resolution with high resolution, and afterwards adds characteristic pyramids to the HRNetnetwork in order to enhance the capability of detecting small objects by utilizing its ability to solve multi-scale problems. The model can estimate a human's pose accurately, according to experimental results, and on the MPII Human Pose dataset, the addition of the feature pyramid enhances the model's detection performance by 0.05. in order to achieve smoothing after adding feature mapping with different resolutions, the detection performance improves by 0.17 after further improvement by adding convolution. For the elbow, it improves by almost 0.5. The model's ability to increase the precision of human pose estimate is supported by all of the outcomes.