A HUMAN POSE ESTIMATION ALGORITHM COMBINED WITH LIGHTWEIGHT ATTENTION MECHANISM

Aimed at the problems of the large amount of model parameters and calculation,high redundancy,and long time-consuming in existing human pose estimation models,a network framework of lightweight attention mechanism is proposed.The lightweight network MobilenetV3 was used to replace the original OpenPose backbone network VGG-1.The two-branch multi-stage convolution neural network framework of OpenPose was compressed.The attention mechanism module CBAM that combined space and channel was introduced,and the speed and accuracy of the model were weighed.Experimental results show that the network model size and floating-point calculation amount under this method are 10.51 MB and 22.65 GFlops,respectively,which are reduced by 79.91％and 83.35％compared with the original OpenPose.Under the COCO2017 test set,this algorithm significantly improves the detection speed on the basis of maintaining high detection accuracy and recall rate.