Abstract: Human pose estimation has been a hot topic in the field of human-computer interaction in recent years. At
present, the common methods for human pose estimation focus on improving the accuracy by increasing the network
complexity. However, the cost-effectiveness of the model was ignored, resulting in high accuracy of the model in
practice but huge consumption of computational resources. In this paper, a model for lightweight hu-man pose estimation
with global pose perception was designed. It has an accuracy of 68.2% AP on the MSCOCO dataset, and the speed
remains at 255 fps, and the parameter amount and FLOPS are 10% and 0.9% that of the OpenPose method, respectively.
In the human pose estimation task, the number of output channels of the network will be set according to the number of
predicted key joints, leading to independent detection of each key joint. Global information, such as the relative position
between key points and the overall layout, is of great significance to the pose estimation task for difficult samples, in which was absent from previous studies. In order to utilize the global pose information, a global pose perception module
was designed to extract the global pose features, and the two-branch network was employed to fuse the global and local
pose features. Experiments show that the lightweight human pose estimation network with global pose perception can
increase the accuracy by 1.5% and 1.3% on the MPII and MSCOCO datasets, respectively.

Key words: human pose estimation, lightweight, global pose perception, two-branch network, feature fusion