  Human pose estimation (HPE), particularly multi-person pose estimation
(MPPE), has been applied in many domains such as human-machine systems.
However, the current MPPE methods generally run on powerful GPU systems and
take a lot of computational costs. Real-time MPPE on mobile devices with
low-performance computing is a challenging task. In this paper, we propose a
lightweight neural network, DIR-BHRNet, for real-time MPPE on smartphones. In
DIR-BHRNet, we design a novel lightweight convolutional module, Dense Inverted
Residual (DIR), to improve accuracy by adding a depthwise convolution and a
shortcut connection into the well-known Inverted Residual, and a novel
efficient neural network structure, Balanced HRNet (BHRNet), to reduce
computational costs by reconfiguring the proper number of convolutional blocks
on each branch. We evaluate DIR-BHRNet on the well-known COCO and CrowdPose
datasets. The results show that DIR-BHRNet outperforms the state-of-the-art
methods in terms of accuracy with a real-time computational cost. Finally, we
implement the DIR-BHRNet on the current mainstream Android smartphones, which
perform more than 10 FPS. The free-used executable file (Android 10), source
code, and a video description of this work are publicly available on the page 1
to facilitate the development of real-time MPPE on smartphones.
