强化先验骨架结构的轻量型高效人体姿态估计
孙雪菲,张瑞峰,关欣,李锵

Lightweight and efficient human pose estimation with enhanced priori skeleton structure
Xuefei SUN,Ruifeng ZHANG,Xin GUAN,Qiang LI
表 4 在COCO验证集上不同网络的平均精度和平均召回率对比结果
Tab.4 Comparison results of average precision and average recall for different networks on COCO validation set
方法 预训练 输入图像尺寸 Np/106 FLOPs/109 AP/% AP0.5/% AP0.75/% APM/% APL/% AR/%
8-stage Hourglass[2] N 256×192 25.1 14.3 66.9
CPN50[3] Y 256×192 27.0 6.2 68.6
Simple Baseline152[4] Y 256×192 68.6 15.7 72.0 89.3 79.8 68.7 78.9 77.8
HRNet(W32)[5] Y 256×192 28.5 7.1 74.4 90.5 81.9 70.8 81.0 79.8
HRNet(W48)[5] Y 256×192 63.6 14.6 75.1 90.6 82.2 71.5 81.8 80.4
RAM-GPRNet(W32)[23] Y 256×192 31.4 7.7 76.0
RAM-GPRNet(W48)[23] Y 256×192 70.0 15.8 76.5
HRFormer-B[24] Y 256×192 43.2 12.2 75.6 90.8 82.8 71.7 82.6 80.8
HRGCNet(W32)[25] Y 256×192 29.6 7.11 76.6 93.6 84.6 73.9 80.7 79.3
HRGCNet(W48)[25] Y 256×192 64.6 14.6 77.4 93.6 84.8 74.6 81.7 80.1
AMHRNet(W32)[26] 256×192 36.4 76.1 91.0 82.7 71.5 82.9 81.2
AMHRNet(W48)[26] 256×192 71.8 76.4 91.1 83.1 72.2 83.3 81.4
本文方法(W32) Y 256×192 21.1 6.3 76.7 93.6 84.8 73.3 81.8 80.7
本文方法(W48) Y 256×192 46.2 12.3 77.4 93.7 85.0 74.4 82.3 81.4
CPN50[3] Y 384×288 13.9 70.6
Simple Baseline152[4] Y 384×288 68.6 35.3 74.3 89.6 81.1 70.5 81.6 79.7
HRNet(W32)[5] Y 384×288 28.5 16.0 75.8 90.6 82.5 72.0 82.7 80.9
HRNet(W48)[5] Y 384×288 63.6 32.9 76.3 90.8 82.9 72.3 83.4 81.2
RAM-GPRNet(W32)[23] Y 384×288 31.4 17.2 77.3
RAM-GPRNet(W48)[23] Y 384×288 70.0 35.6 77.7
HRFormer-B[24] Y 384×288 43.2 26.8 77.2 91.0 83.6 73.2 84.2 82.0
HRGCNet(W32)[25] Y 384×288 29.6 16.1 78.0 93.6 84.8 75.0 82.6 80.5
HRGCNet(W48)[25] Y 384×288 64.6 32.9 78.4 93.6 85.8 75.3 83.5 81.3
本文方法(W32) Y 384×288 21.1 14.8 78.2 93.7 85.0 75.4 82.9 81.2
本文方法(W48) Y 384×288 46.2 28.5 78.5 93.7 85.8 75.5 83.7 81.9