Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (8): 2548-2555.DOI: 10.11772/j.issn.1001-9081.2021050805
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Feiyu YANG1,2(), Zhan SONG1, Zhenzhong XIAO2, Yaoyang MO2, Yu CHEN2, Zhe PAN2, Min ZHANG2, Yao ZHANG2, Beibei QIAN2, Chaowei TANG3, Wu JIN3
Received:
2021-05-17
Revised:
2021-11-04
Accepted:
2021-11-04
Online:
2022-08-09
Published:
2022-08-10
Contact:
Feiyu YANG
About author:
YANG Feiyu, born in 1990, Ph. D. His research interests include human pose estimation, image segmentation.
杨飞宇1,2(), 宋展1, 肖振中2, 莫曜阳2, 陈宇2, 潘哲2, 张敏2, 张遥2, 钱贝贝2, 汤朝伟3, 金武3
通讯作者:
杨飞宇
作者简介:
杨飞宇(1990—),男,广东深圳人,博士,主要研究方向:人体姿态估计、图像分割;CLC Number:
Feiyu YANG, Zhan SONG, Zhenzhong XIAO, Yaoyang MO, Yu CHEN, Zhe PAN, Min ZHANG, Yao ZHANG, Beibei QIAN, Chaowei TANG, Wu JIN. Rethinking errors in human pose estimation heatmap[J]. Journal of Computer Applications, 2022, 42(8): 2548-2555.
杨飞宇, 宋展, 肖振中, 莫曜阳, 陈宇, 潘哲, 张敏, 张遥, 钱贝贝, 汤朝伟, 金武. 对人体姿态估计热图误差的再思考[J]. 《计算机应用》唯一官方网站, 2022, 42(8): 2548-2555.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021050805
模型 | 输入分辨率 | |
---|---|---|
ResNet-50 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
ResNet-101 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
ResNet-152 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
HR-W32 | 256×192 | 4 |
384×288 | 5 | |
HR-W48 | 256×192 | 4 |
384×288 | 5 |
Tab. 1 Optimal error compensation factor Δopt value of each model
模型 | 输入分辨率 | |
---|---|---|
ResNet-50 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
ResNet-101 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
ResNet-152 | 256×192 | 4 |
256×256 | 4 | |
384×288 | 5 | |
HR-W32 | 256×192 | 4 |
384×288 | 5 | |
HR-W48 | 256×192 | 4 |
384×288 | 5 |
模型 | 输入分辨率 | 算法 | 精度/% | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AP | AP50 | AP75 | APM | APL | AR | AR50 | AR75 | ARM | ARL | |||
ResNet-50 | 256×192 | 标准算法 | 65.34 | 90.37 | 74.48 | 63.25 | 68.59 | 69.32 | 91.85 | 77.96 | 66.57 | 73.48 |
Shifting算法 | 66.80 | 90.43 | 75.74 | 65.15 | 70.28 | 70.84 | 91.99 | 78.90 | 68.09 | 75.00 | ||
DARK算法 | 68.40 | 91.38 | 76.89 | 66.60 | 71.59 | 72.01 | 92.07 | 79.72 | 69.30 | 76.14 | ||
本文算法 | 70.63 | 91.40 | 78.17 | 68.27 | 74.66 | 74.11 | 92.24 | 80.81 | 70.98 | 78.85 | ||
384×288 | 标准算法 | 69.85 | 91.46 | 77.07 | 66.86 | 74.66 | 73.28 | 92.48 | 79.83 | 69.55 | 78.80 | |
Shifting算法 | 70.71 | 91.47 | 78.01 | 67.45 | 75.55 | 73.96 | 92.51 | 80.26 | 70.18 | 79.56 | ||
DARK算法 | 71.49 | 91.47 | 78.20 | 68.43 | 76.50 | 74.71 | 92.66 | 80.79 | 70.93 | 80.35 | ||
本文算法 | 72.92 | 91.52 | 79.41 | 69.20 | 78.45 | 75.80 | 92.87 | 81.72 | 71.72 | 81.86 | ||
ResNet-101 | 256×192 | 标准算法 | 66.60 | 91.45 | 75.77 | 65.21 | 69.60 | 70.54 | 92.46 | 78.84 | 68.04 | 74.35 |
Shifting算法 | 68.43 | 91.44 | 77.89 | 66.77 | 71.40 | 72.06 | 92.44 | 80.05 | 69.60 | 75.86 | ||
DARK算法 | 69.30 | 91.48 | 78.08 | 67.85 | 72.60 | 73.13 | 92.66 | 80.72 | 70.66 | 76.99 | ||
本文算法 | 71.98 | 92.48 | 79.32 | 69.60 | 75.73 | 75.31 | 93.15 | 81.85 | 72.44 | 79.73 | ||
384×288 | 标准算法 | 71.63 | 92.44 | 80.19 | 69.04 | 76.02 | 75.07 | 93.25 | 82.24 | 71.75 | 80.12 | |
Shifting算法 | 72.42 | 92.45 | 80.25 | 69.78 | 76.66 | 75.76 | 93.26 | 82.51 | 72.49 | 80.75 | ||
DARK算法 | 73.22 | 92.47 | 80.35 | 70.70 | 77.68 | 76.51 | 93.31 | 82.97 | 73.20 | 81.56 | ||
本文算法 | 74.52 | 92.47 | 81.40 | 71.44 | 79.40 | 77.55 | 93.42 | 83.61 | 73.97 | 82.99 | ||
ResNet-152 | 256×192 | 标准算法 | 67.42 | 91.48 | 76.75 | 65.51 | 70.85 | 71.26 | 92.66 | 79.83 | 68.63 | 75.28 |
Shifting算法 | 68.86 | 91.52 | 77.86 | 67.10 | 72.23 | 72.60 | 92.85 | 80.68 | 70.02 | 76.55 | ||
DARK算法 | 70.17 | 92.47 | 78.93 | 68.17 | 73.59 | 73.74 | 93.03 | 81.27 | 71.13 | 77.77 | ||
本文算法 | 72.75 | 92.51 | 80.34 | 70.00 | 76.84 | 75.95 | 93.14 | 82.68 | 72.84 | 80.68 | ||
384×288 | 标准算法 | 72.83 | 92.50 | 81.38 | 70.24 | 76.99 | 76.15 | 93.64 | 83.50 | 72.95 | 81.00 | |
Shifting算法 | 73.51 | 92.52 | 81.47 | 70.96 | 77.74 | 76.80 | 93.73 | 83.80 | 73.60 | 81.67 | ||
DARK算法 | 74.26 | 92.54 | 82.44 | 71.88 | 78.63 | 77.50 | 93.77 | 84.32 | 74.34 | 82.31 | ||
本文算法 | 75.48 | 92.54 | 82.59 | 72.57 | 80.33 | 78.50 | 93.84 | 84.70 | 75.05 | 83.75 | ||
HR-W32 | 256×192 | 标准算法 | 69.66 | 92.49 | 79.02 | 67.87 | 73.16 | 73.42 | 93.77 | 81.99 | 70.79 | 77.48 |
Shifting算法 | 71.33 | 92.49 | 81.11 | 69.63 | 74.68 | 74.85 | 93.78 | 83.01 | 72.21 | 78.95 | ||
DARK算法 | 72.74 | 92.51 | 81.41 | 70.85 | 76.57 | 76.24 | 93.83 | 83.82 | 73.46 | 80.53 | ||
本文算法 | 75.47 | 93.49 | 83.50 | 72.86 | 79.52 | 78.35 | 94.05 | 85.11 | 75.26 | 83.13 | ||
384×288 | 标准算法 | 73.53 | 92.54 | 82.21 | 71.24 | 77.74 | 76.94 | 93.88 | 84.15 | 73.69 | 81.92 | |
Shifting算法 | 74.45 | 92.54 | 82.33 | 71.84 | 78.62 | 77.69 | 93.92 | 84.49 | 74.45 | 82.66 | ||
DARK算法 | 75.75 | 93.55 | 83.33 | 73.05 | 79.92 | 78.71 | 94.16 | 85.06 | 75.45 | 83.72 | ||
本文算法 | 77.00 | 93.54 | 83.67 | 73.86 | 81.86 | 79.71 | 94.14 | 85.64 | 76.17 | 85.13 | ||
HR-W48 | 256×192 | 标准算法 | 69.86 | 92.48 | 79.79 | 68.12 | 73.31 | 73.70 | 93.73 | 82.31 | 70.90 | 77.92 |
Shifting算法 | 71.53 | 92.50 | 81.03 | 69.56 | 75.05 | 75.23 | 93.78 | 83.28 | 72.38 | 79.55 | ||
DARK算法 | 72.84 | 92.52 | 82.11 | 71.18 | 76.36 | 76.51 | 93.86 | 84.18 | 73.70 | 80.81 | ||
本文算法 | 75.70 | 93.50 | 83.56 | 73.05 | 79.92 | 78.71 | 94.07 | 85.53 | 75.44 | 83.68 | ||
384×288 | 标准算法 | 74.42 | 93.48 | 82.41 | 71.72 | 78.60 | 77.60 | 94.05 | 84.65 | 74.41 | 82.49 | |
Shifting算法 | 75.18 | 93.48 | 82.53 | 72.54 | 79.39 | 78.28 | 94.11 | 84.93 | 75.11 | 83.16 | ||
DARK算法 | 76.15 | 93.50 | 83.69 | 73.59 | 80.46 | 79.15 | 94.11 | 85.67 | 75.99 | 84.02 | ||
本文算法 | 77.23 | 93.52 | 83.74 | 74.15 | 82.25 | 80.07 | 94.24 | 85.97 | 76.61 | 85.41 |
Tab. 2 Accuracy on COCO validation dataset (validation process without flip strategy)
模型 | 输入分辨率 | 算法 | 精度/% | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AP | AP50 | AP75 | APM | APL | AR | AR50 | AR75 | ARM | ARL | |||
ResNet-50 | 256×192 | 标准算法 | 65.34 | 90.37 | 74.48 | 63.25 | 68.59 | 69.32 | 91.85 | 77.96 | 66.57 | 73.48 |
Shifting算法 | 66.80 | 90.43 | 75.74 | 65.15 | 70.28 | 70.84 | 91.99 | 78.90 | 68.09 | 75.00 | ||
DARK算法 | 68.40 | 91.38 | 76.89 | 66.60 | 71.59 | 72.01 | 92.07 | 79.72 | 69.30 | 76.14 | ||
本文算法 | 70.63 | 91.40 | 78.17 | 68.27 | 74.66 | 74.11 | 92.24 | 80.81 | 70.98 | 78.85 | ||
384×288 | 标准算法 | 69.85 | 91.46 | 77.07 | 66.86 | 74.66 | 73.28 | 92.48 | 79.83 | 69.55 | 78.80 | |
Shifting算法 | 70.71 | 91.47 | 78.01 | 67.45 | 75.55 | 73.96 | 92.51 | 80.26 | 70.18 | 79.56 | ||
DARK算法 | 71.49 | 91.47 | 78.20 | 68.43 | 76.50 | 74.71 | 92.66 | 80.79 | 70.93 | 80.35 | ||
本文算法 | 72.92 | 91.52 | 79.41 | 69.20 | 78.45 | 75.80 | 92.87 | 81.72 | 71.72 | 81.86 | ||
ResNet-101 | 256×192 | 标准算法 | 66.60 | 91.45 | 75.77 | 65.21 | 69.60 | 70.54 | 92.46 | 78.84 | 68.04 | 74.35 |
Shifting算法 | 68.43 | 91.44 | 77.89 | 66.77 | 71.40 | 72.06 | 92.44 | 80.05 | 69.60 | 75.86 | ||
DARK算法 | 69.30 | 91.48 | 78.08 | 67.85 | 72.60 | 73.13 | 92.66 | 80.72 | 70.66 | 76.99 | ||
本文算法 | 71.98 | 92.48 | 79.32 | 69.60 | 75.73 | 75.31 | 93.15 | 81.85 | 72.44 | 79.73 | ||
384×288 | 标准算法 | 71.63 | 92.44 | 80.19 | 69.04 | 76.02 | 75.07 | 93.25 | 82.24 | 71.75 | 80.12 | |
Shifting算法 | 72.42 | 92.45 | 80.25 | 69.78 | 76.66 | 75.76 | 93.26 | 82.51 | 72.49 | 80.75 | ||
DARK算法 | 73.22 | 92.47 | 80.35 | 70.70 | 77.68 | 76.51 | 93.31 | 82.97 | 73.20 | 81.56 | ||
本文算法 | 74.52 | 92.47 | 81.40 | 71.44 | 79.40 | 77.55 | 93.42 | 83.61 | 73.97 | 82.99 | ||
ResNet-152 | 256×192 | 标准算法 | 67.42 | 91.48 | 76.75 | 65.51 | 70.85 | 71.26 | 92.66 | 79.83 | 68.63 | 75.28 |
Shifting算法 | 68.86 | 91.52 | 77.86 | 67.10 | 72.23 | 72.60 | 92.85 | 80.68 | 70.02 | 76.55 | ||
DARK算法 | 70.17 | 92.47 | 78.93 | 68.17 | 73.59 | 73.74 | 93.03 | 81.27 | 71.13 | 77.77 | ||
本文算法 | 72.75 | 92.51 | 80.34 | 70.00 | 76.84 | 75.95 | 93.14 | 82.68 | 72.84 | 80.68 | ||
384×288 | 标准算法 | 72.83 | 92.50 | 81.38 | 70.24 | 76.99 | 76.15 | 93.64 | 83.50 | 72.95 | 81.00 | |
Shifting算法 | 73.51 | 92.52 | 81.47 | 70.96 | 77.74 | 76.80 | 93.73 | 83.80 | 73.60 | 81.67 | ||
DARK算法 | 74.26 | 92.54 | 82.44 | 71.88 | 78.63 | 77.50 | 93.77 | 84.32 | 74.34 | 82.31 | ||
本文算法 | 75.48 | 92.54 | 82.59 | 72.57 | 80.33 | 78.50 | 93.84 | 84.70 | 75.05 | 83.75 | ||
HR-W32 | 256×192 | 标准算法 | 69.66 | 92.49 | 79.02 | 67.87 | 73.16 | 73.42 | 93.77 | 81.99 | 70.79 | 77.48 |
Shifting算法 | 71.33 | 92.49 | 81.11 | 69.63 | 74.68 | 74.85 | 93.78 | 83.01 | 72.21 | 78.95 | ||
DARK算法 | 72.74 | 92.51 | 81.41 | 70.85 | 76.57 | 76.24 | 93.83 | 83.82 | 73.46 | 80.53 | ||
本文算法 | 75.47 | 93.49 | 83.50 | 72.86 | 79.52 | 78.35 | 94.05 | 85.11 | 75.26 | 83.13 | ||
384×288 | 标准算法 | 73.53 | 92.54 | 82.21 | 71.24 | 77.74 | 76.94 | 93.88 | 84.15 | 73.69 | 81.92 | |
Shifting算法 | 74.45 | 92.54 | 82.33 | 71.84 | 78.62 | 77.69 | 93.92 | 84.49 | 74.45 | 82.66 | ||
DARK算法 | 75.75 | 93.55 | 83.33 | 73.05 | 79.92 | 78.71 | 94.16 | 85.06 | 75.45 | 83.72 | ||
本文算法 | 77.00 | 93.54 | 83.67 | 73.86 | 81.86 | 79.71 | 94.14 | 85.64 | 76.17 | 85.13 | ||
HR-W48 | 256×192 | 标准算法 | 69.86 | 92.48 | 79.79 | 68.12 | 73.31 | 73.70 | 93.73 | 82.31 | 70.90 | 77.92 |
Shifting算法 | 71.53 | 92.50 | 81.03 | 69.56 | 75.05 | 75.23 | 93.78 | 83.28 | 72.38 | 79.55 | ||
DARK算法 | 72.84 | 92.52 | 82.11 | 71.18 | 76.36 | 76.51 | 93.86 | 84.18 | 73.70 | 80.81 | ||
本文算法 | 75.70 | 93.50 | 83.56 | 73.05 | 79.92 | 78.71 | 94.07 | 85.53 | 75.44 | 83.68 | ||
384×288 | 标准算法 | 74.42 | 93.48 | 82.41 | 71.72 | 78.60 | 77.60 | 94.05 | 84.65 | 74.41 | 82.49 | |
Shifting算法 | 75.18 | 93.48 | 82.53 | 72.54 | 79.39 | 78.28 | 94.11 | 84.93 | 75.11 | 83.16 | ||
DARK算法 | 76.15 | 93.50 | 83.69 | 73.59 | 80.46 | 79.15 | 94.11 | 85.67 | 75.99 | 84.02 | ||
本文算法 | 77.23 | 93.52 | 83.74 | 74.15 | 82.25 | 80.07 | 94.24 | 85.97 | 76.61 | 85.41 |
模型 | 算法 | PCKh | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Head | Shoul. | Elbow | Wrist | Hip | Knee | Ankle | PCKh0.1 | PCKh0.5 | ||
ResNet-50 | 标准算法 | 96.04 | 94.19 | 87.25 | 81.34 | 86.15 | 81.60 | 78.32 | 21.55 | 86.99 |
Shifting算法 | 96.04 | 94.34 | 87.35 | 81.53 | 86.41 | 81.85 | 78.48 | 23.40 | 87.15 | |
DARK算法 | 96.15 | 94.53 | 87.76 | 81.87 | 86.76 | 82.49 | 78.81 | 24.48 | 87.48 | |
本文算法 | 95.87 | 94.87 | 88.44 | 82.05 | 87.62 | 83.22 | 79.48 | 31.69 | 87.95 | |
ResNet-101 | 标准算法 | 96.35 | 94.62 | 87.40 | 82.41 | 85.72 | 82.35 | 78.77 | 22.07 | 87.36 |
Shifting算法 | 96.59 | 94.58 | 87.69 | 82.39 | 86.22 | 82.71 | 78.98 | 23.66 | 87.56 | |
DARK算法 | 96.32 | 94.72 | 88.07 | 82.85 | 86.71 | 83.16 | 79.24 | 24.82 | 87.85 | |
本文算法 | 96.28 | 94.80 | 88.55 | 83.42 | 87.54 | 83.42 | 79.74 | 32.09 | 88.25 | |
ResNet-152 | 标准算法 | 96.62 | 95.02 | 88.27 | 82.70 | 86.38 | 83.30 | 79.85 | 22.55 | 87.98 |
Shifting算法 | 96.62 | 95.31 | 88.56 | 82.99 | 86.91 | 83.58 | 79.83 | 24.31 | 88.23 | |
DARK算法 | 96.73 | 95.33 | 88.80 | 83.66 | 87.02 | 83.78 | 80.63 | 25.28 | 88.50 | |
本文算法 | 96.56 | 95.67 | 88.97 | 83.85 | 87.99 | 84.14 | 80.52 | 33.07 | 88.78 | |
HR-W32 | 标准算法 | 96.79 | 95.06 | 89.08 | 84.29 | 86.01 | 84.40 | 81.39 | 23.49 | 88.61 |
Shifting算法 | 96.93 | 95.25 | 89.06 | 84.39 | 86.43 | 84.89 | 81.58 | 25.36 | 88.81 | |
DARK算法 | 96.97 | 95.40 | 89.57 | 85.03 | 87.04 | 85.67 | 82.03 | 27.38 | 89.25 | |
本文算法 | 96.86 | 95.58 | 89.98 | 85.49 | 87.83 | 86.18 | 82.59 | 35.80 | 89.67 |
Tab. 3 PCKh accuracy on MPII dataset (validation process without flip strategy)
模型 | 算法 | PCKh | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Head | Shoul. | Elbow | Wrist | Hip | Knee | Ankle | PCKh0.1 | PCKh0.5 | ||
ResNet-50 | 标准算法 | 96.04 | 94.19 | 87.25 | 81.34 | 86.15 | 81.60 | 78.32 | 21.55 | 86.99 |
Shifting算法 | 96.04 | 94.34 | 87.35 | 81.53 | 86.41 | 81.85 | 78.48 | 23.40 | 87.15 | |
DARK算法 | 96.15 | 94.53 | 87.76 | 81.87 | 86.76 | 82.49 | 78.81 | 24.48 | 87.48 | |
本文算法 | 95.87 | 94.87 | 88.44 | 82.05 | 87.62 | 83.22 | 79.48 | 31.69 | 87.95 | |
ResNet-101 | 标准算法 | 96.35 | 94.62 | 87.40 | 82.41 | 85.72 | 82.35 | 78.77 | 22.07 | 87.36 |
Shifting算法 | 96.59 | 94.58 | 87.69 | 82.39 | 86.22 | 82.71 | 78.98 | 23.66 | 87.56 | |
DARK算法 | 96.32 | 94.72 | 88.07 | 82.85 | 86.71 | 83.16 | 79.24 | 24.82 | 87.85 | |
本文算法 | 96.28 | 94.80 | 88.55 | 83.42 | 87.54 | 83.42 | 79.74 | 32.09 | 88.25 | |
ResNet-152 | 标准算法 | 96.62 | 95.02 | 88.27 | 82.70 | 86.38 | 83.30 | 79.85 | 22.55 | 87.98 |
Shifting算法 | 96.62 | 95.31 | 88.56 | 82.99 | 86.91 | 83.58 | 79.83 | 24.31 | 88.23 | |
DARK算法 | 96.73 | 95.33 | 88.80 | 83.66 | 87.02 | 83.78 | 80.63 | 25.28 | 88.50 | |
本文算法 | 96.56 | 95.67 | 88.97 | 83.85 | 87.99 | 84.14 | 80.52 | 33.07 | 88.78 | |
HR-W32 | 标准算法 | 96.79 | 95.06 | 89.08 | 84.29 | 86.01 | 84.40 | 81.39 | 23.49 | 88.61 |
Shifting算法 | 96.93 | 95.25 | 89.06 | 84.39 | 86.43 | 84.89 | 81.58 | 25.36 | 88.81 | |
DARK算法 | 96.97 | 95.40 | 89.57 | 85.03 | 87.04 | 85.67 | 82.03 | 27.38 | 89.25 | |
本文算法 | 96.86 | 95.58 | 89.98 | 85.49 | 87.83 | 86.18 | 82.59 | 35.80 | 89.67 |
算法 | 分辨率 | 高斯平滑 | ResNet-50 | ResNet-101 | ResNet-152 | HR-W32 | ||||
---|---|---|---|---|---|---|---|---|---|---|
精度/% | 相差百分点 | 精度/% | 相差百分点 | 精度/% | 相差百分点 | 精度/% | 相差百分点 | |||
DARK算法 | 256×192 | 68.40 | 0.52↓ | 69.30 | 0.09↓ | 70.17 | 0.26↓ | 72.74 | 0.50↓ | |
67.88 | 69.21 | 69.91 | 72.24 | |||||||
384×288 | 71.49 | 0.41↓ | 73.22 | 0.24↓ | 74.26 | 0.13↓ | 75.75 | 0.75↓ | ||
71.08 | 72.98 | 74.13 | 75.00 | |||||||
本文算法 | 256×192 | 70.69 | 0.06↓ | 71.96 | 0.02↑ | 72.76 | 0.01↓ | 75.43 | 0.04↑ | |
70.63 | 71.98 | 72.75 | 75.47 | |||||||
384×288 | 72.84 | 0.08↑ | 74.53 | 0.01↓ | 75.31 | 0.17↑ | 77.01 | 0.01↓ | ||
72.92 | 74.52 | 75.48 | 77.00 |
Tab. 4 Influence of resolution, Gaussian smoothing and backbone network on algorithm accuracy
算法 | 分辨率 | 高斯平滑 | ResNet-50 | ResNet-101 | ResNet-152 | HR-W32 | ||||
---|---|---|---|---|---|---|---|---|---|---|
精度/% | 相差百分点 | 精度/% | 相差百分点 | 精度/% | 相差百分点 | 精度/% | 相差百分点 | |||
DARK算法 | 256×192 | 68.40 | 0.52↓ | 69.30 | 0.09↓ | 70.17 | 0.26↓ | 72.74 | 0.50↓ | |
67.88 | 69.21 | 69.91 | 72.24 | |||||||
384×288 | 71.49 | 0.41↓ | 73.22 | 0.24↓ | 74.26 | 0.13↓ | 75.75 | 0.75↓ | ||
71.08 | 72.98 | 74.13 | 75.00 | |||||||
本文算法 | 256×192 | 70.69 | 0.06↓ | 71.96 | 0.02↑ | 72.76 | 0.01↓ | 75.43 | 0.04↑ | |
70.63 | 71.98 | 72.75 | 75.47 | |||||||
384×288 | 72.84 | 0.08↑ | 74.53 | 0.01↓ | 75.31 | 0.17↑ | 77.01 | 0.01↓ | ||
72.92 | 74.52 | 75.48 | 77.00 |
算法 | 每帧额外耗时 |
---|---|
Shifting算法 | 0.3 |
DARK算法 | 3.0 |
本文算法 | 1.4 |
Tab. 5 Extra running time of different methods beyond standard method
算法 | 每帧额外耗时 |
---|---|
Shifting算法 | 0.3 |
DARK算法 | 3.0 |
本文算法 | 1.4 |
1 | RUMELHART D E, HINTON G E, WILLIAMS R J. Learning internal representations by error propagation[M]// RUMELHART D E, McCLELLAND J L, PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. Cambridge: MIT Press, 1986: 318-362. |
2 | SUN K, XIAO B, LIU D, et al. Deep high-resolution representation learning for human pose estimation [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 5686-5696. 10.1109/cvpr.2019.00584 |
3 | LI W B, WANG Z C, YIN B Y, et al. Rethinking on multi-stage networks for human pose estimation[EB/OL]. (2019-05-30) [2021-03-20]. . |
4 | CHEN Y L, WANG Z C, PENG Y X, et al. Cascaded pyramid network for multi-person pose estimation [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7103-7112. 10.1109/cvpr.2018.00742 |
5 | LIFSHITZ I, FETAYA E, ULLMAN S. Human pose estimation using deep consensus voting [C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS 9906. Cham: Springer, 2016: 246-260. |
6 | ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points[EB/OL]. (2019-04-25) [2021-03-20]. . |
7 | DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database [C]// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 248-255. 10.1109/cvpr.2009.5206848 |
8 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. 10.1109/cvpr.2016.90 |
9 | SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 1-9. 10.1109/cvpr.2015.7298594 |
10 | HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988. 10.1109/iccv.2017.322 |
11 | REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 779-788. 10.1109/cvpr.2016.91 |
12 | LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multiBox detector [C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS 9905. Cham: Springer, 2016: 21-37. |
13 | CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. 10.1109/tpami.2017.2699184 |
14 | RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation [C]// Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention, LNCS 9351. Cham: Springer, 2015: 234-241. |
15 | NEWELL A, YANG K Y, DENG J. Stacked hourglass networks for human pose estimation [C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS 9912. Cham: Springer, 2016: 483-499. |
16 | ZHANG F, ZHU X T, DAI H B, et al. Distribution-aware coordinate representation for human pose estimation [C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 7091-7100. 10.1109/cvpr42600.2020.00712 |
17 | XIAO B, WU H P, WEI Y C. Simple baselines for human pose estimation and tracking [C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11210. Cham: Springer, 2018: 472-487. |
18 | WEI S E, RAMAKRISHNA V, KANADE T, et al. Convolutional pose machines [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 4724-4732. 10.1109/cvpr.2016.511 |
19 | CAO Z, SIMON T, WEI S E, et al. Realtime multi-person 2D pose estimation using part affinity fields [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 1302-1310. 10.1109/cvpr.2017.143 |
20 | NEWELL A, HUANG Z A, DENG J. Associative embedding: end-to-end learning for joint detection and grouping [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 2274-2284. |
21 | SEKII T. Pose proposal networks [C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11217. Cham: Springer, 2018: 350-366. |
22 | 张小娜,吴庆涛.基于深度学习的自顶向下人体姿态估计算法[J].电子测量技术, 2021, 44(9): 105-109. |
ZHANG X N, WU Q T. Top-down human pose estimation algorithm based on deep learning[J]. Electronic Measurement Technology, 2021, 44(9): 105-109. | |
23 | 田宇.基于卷积神经网络的人体姿态估计算法研究[D].天津:天津理工大学, 2021: 35-46. |
TIAN Y. Body posture estimation on convolutional neural network[D]. Tianjin: Tianjin University of Technology, 2021: 35-46. | |
24 | TOMPSON J, JAIN A, LeCUN Y, et al. Joint training of a convolutional network and a graphical model for human pose estimation [C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014: 1799-1807. |
25 | LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context [C]// Proceedings of the 2014 European Conference on Computer Vision, LNCS 8693. Cham: Springer, 2014: 740-755. |
26 | ANDRILUKA M, PISHCHULIN L, GEHLER P, et al. 2D human pose estimation: new benchmark and state of the art analysis [C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 3686-3693. 10.1109/cvpr.2014.471 |
[1] | Na WANG, Lin JIANG, Yuancheng LI, Yun ZHU. Optimization of tensor virtual machine operator fusion based on graph rewriting and fusion exploration [J]. Journal of Computer Applications, 2024, 44(9): 2802-2809. |
[2] | Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910. |
[3] | Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718. |
[4] | Rui ZHANG, Pengyun ZHANG, Meirong GAO. Self-optimized dual-modal multi-channel non-deep vestibular schwannoma recognition model [J]. Journal of Computer Applications, 2024, 44(9): 2975-2982. |
[5] | Jinjin LI, Guoming SANG, Yijia ZHANG. Multi-domain fake news detection model enhanced by APK-CNN and Transformer [J]. Journal of Computer Applications, 2024, 44(9): 2674-2682. |
[6] | Guanglei YAO, Juxia XIONG, Guowu YANG. Flower pollination algorithm based on neural network optimization [J]. Journal of Computer Applications, 2024, 44(9): 2829-2837. |
[7] | Ying HUANG, Jiayu YANG, Jiahao JIN, Bangrui WAN. Siamese mixed information fusion algorithm for RGBT tracking [J]. Journal of Computer Applications, 2024, 44(9): 2878-2885. |
[8] | Xingyao YANG, Yu CHEN, Jiong YU, Zulian ZHANG, Jiaying CHEN, Dongxiao WANG. Recommendation model combining self-features and contrastive learning [J]. Journal of Computer Applications, 2024, 44(9): 2704-2710. |
[9] | Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974. |
[10] | Hang YANG, Wanggen LI, Gensheng ZHANG, Zhige WANG, Xin KAI. Multi-layer information interactive fusion algorithm based on graph neural network for session-based recommendation [J]. Journal of Computer Applications, 2024, 44(9): 2719-2725. |
[11] | Yu DU, Yan ZHU. Constructing pre-trained dynamic graph neural network to predict disappearance of academic cooperation behavior [J]. Journal of Computer Applications, 2024, 44(9): 2726-2731. |
[12] | Yubo ZHAO, Liping ZHANG, Sheng YAN, Min HOU, Mao GAO. Relation extraction between discipline knowledge entities based on improved piecewise convolutional neural network and knowledge distillation [J]. Journal of Computer Applications, 2024, 44(8): 2421-2429. |
[13] | Zheyuan SHEN, Keke YANG, Jing LI. Personalized federated learning method based on dual stream neural network [J]. Journal of Computer Applications, 2024, 44(8): 2319-2325. |
[14] | Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499. |
[15] | Ying YANG, Xiaoyan HAO, Dan YU, Yao MA, Yongle CHEN. Graph data generation approach for graph neural network model extraction attacks [J]. Journal of Computer Applications, 2024, 44(8): 2483-2492. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||