Distributed Human Pose Estimation Based on BlazePose

Abstract

Abstract The lightweight of human pose estimation model is one current research hotspot in the field of computer vision. The emphasis of current research is to promise lightweight model while improving the network performance. In view of computing and processing performance of monocular static image pose estimation method deployed in mobile devices, a distributed human pose estimation method was proposed. The distributed framework was constructed by simplifying BlazePose as the core, and the 5×5 convolution kernels were run for feature downsampling in a multi-thread simulated distributed environment. The computing power output of mobile devices was reduced by distributed simulation. Experimental results showed that the proposed model had 3%~11% performance improvement compared with other models in the simulation environment. Compared with the traditional human pose estimation model, the proposed model reconstructs the neural network deployment framework, thus indirectly enabling stable recognition of the deployed equipment under low computing power.