Human Pose Estimation based on Attention Multi-resolution Network

Congcong Zhang, Ning He, Qixiang Sun, Xiaojie Yin, Ke Lu

Abstract: Recently, multi-resolution neural networks, which combine features of different resolutions, have achieved good results in human pose estimation tasks. In this paper, we propose an attention-mechanism-based multi-resolution network, which adds an attention mechanism to the High-Resolution Network (HRNet) to enhance the feature representation of the network. It improves the ability of networks with different resolutions to extract key features from images, and causes the output to contain more effective multi-resolution representation information, so that the corresponding point positions of human joints can be estimated more accurately. Experiments on the MPII and COCO datasets, and verification on the MPII datasets, obtained an average accuracy of 90.3% under the [email protected] evaluation standard, and good results were also achieved on the COCO dataset (with an AP of 76.5). The experimental results show that our network model is effective in improving the accuracy of key point estimation in the human pose estimation task.