强化先验骨架结构的轻量型高效人体姿态估计
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孙雪菲,张瑞峰,关欣,李锵
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Lightweight and efficient human pose estimation with enhanced priori skeleton structure
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Xuefei SUN,Ruifeng ZHANG,Xin GUAN,Qiang LI
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| 表 5 在COCO测试集上不同网络的平均精度和平均召回率对比结果 |
| Tab.5 Comparison results of average precision and average recall for different networks on COCO test set |
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| 方法 | 预训练 | 输入图像尺寸 | Np/106 | FLOPs/109 | AP/% | AP0.5/% | AP0.75/% | APM/% | APL/% | AR/% | | CPN50[3] | — | 384×288 | — | — | 72.6 | 86.1 | 69.7 | 78.3 | 64.1 | — | | Simple Baseline152[4] | Y | 256×192 | 68.6 | 15.7 | 71.6 | 91.2 | 80.1 | 68.7 | 77.2 | 77.3 | | HRNet(W32)[5] | Y | 384×288 | 28.5 | 16.0 | 74.9 | 92.5 | 82.8 | 71.3 | 80.9 | 80.1 | | HRNet(W48)[5] | Y | 384×288 | 63.6 | 32.9 | 75.5 | 92.5 | 83.3 | 71.9 | 81.5 | 80.5 | | RAM-GPRNet(W32)[23] | Y | 384×288 | 31.4 | 17.2 | 76.5 | — | — | — | — | — | | RAM-GPRNet(W48)[23] | Y | 384×288 | 70.0 | 35.6 | 77.0 | — | — | — | — | — | | HRFormer-B[24] | Y | 384×288 | 43.2 | 26.8 | 76.2 | 92.7 | 83.8 | 72.5 | 82.3 | 81.2 | | HRGCNet(W32)[25] | Y | 384×288 | 29.6 | 16.1 | 77.9 | 93.6 | 84.8 | 74.8 | 82.9 | 80.6 | | HRGCNet(W48)[25] | Y | 384×288 | 64.6 | 32.9 | 78.3 | 93.6 | 85.7 | 75.3 | 83.5 | 81.2 | | 本文方法(W32) | Y | 384×288 | 21.1 | 14.8 | 78.1 | 93.6 | 85.0 | 75.2 | 83.1 | 81.2 | | 本文方法(W48) | Y | 384×288 | 46.2 | 28.5 | 78.4 | 93.7 | 85.5 | 75.5 | 83.6 | 81.7 |
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