Abstract: Human pose estimation,especially multi-person pose estimation,is gradually penetrating into various aspects,such as education and sports.High-precision and lightweight multi-person pose estimation is a current research hotspot.Generally,bottom-up multi-person pose estimation method has strong real-time performance,however,its accuracy is not high and the network structure is huge.For the key point association problem,this paper proposes a few parameters and efficient pose estimation matching network.This network improves the basic ResNet module in the encoding stage to obtain the layer structure.Using these structures to extract features can greatly reduce the model’s parameter amount.In the decoding stage,a specially designed deconvolution structure is used,and residual connections are added to the entire network,which greatly improves the accuracy of the network.The whole algorithm can correctly match the heat map of key points to everyone,and obtain the final human key point estimate.The proposed model is a portable and efficient human keypoint matching network,because its mAP value on the ground truth of the COCO dataset is as high as 89.7,and the parameters are only 8.01 M.Compared with the current best bottom-up multi-person pose estimation method,the proposed model improves accuracy mAP value by 0.5 and reduces to 1/10 of the original in terms of para-meters.The proposed model uses the COCO 2017 and COCO 2014 datasets to train and verify,andachieves high accuracy.It shows that the proposed model is suitable for the input of heat maps of key points of various human bodies,and can get good results.In addition,this paper designs a variety of ablation experiments for different layer structures of the network model.The lightest structural parameter is only 1.28 M,and the accuracy mAP value can reach 81.8.

Key words: COCO datasets, Heat map, Human pose estimation, mAP value, Matching Algorithm