Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2058-2064.DOI: 10.11772/j.issn.1001-9081.2021050798
• Artificial intelligence • Previous Articles
Zhenyu WANG, Lei ZHANG(), Wenbin GAO, Weiming QUAN
Received:
2021-05-17
Revised:
2021-09-13
Accepted:
2021-09-22
Online:
2021-09-13
Published:
2022-07-10
Contact:
Lei ZHANG
About author:
WANG Zhenyu, born in 1996, M. S. candidate. His research interests include deep learning, pattern recognition, natural language processing.Supported by:
通讯作者:
张雷
作者简介:
王震宇(1996—),男,江苏扬州人,硕士研究生,主要研究方向:深度学习、模式识别、自然语言处理基金资助:
CLC Number:
Zhenyu WANG, Lei ZHANG, Wenbin GAO, Weiming QUAN. Human activity recognition based on progressive neural architecture search[J]. Journal of Computer Applications, 2022, 42(7): 2058-2064.
王震宇, 张雷, 高文彬, 权威铭. 基于渐进式神经网络架构搜索的人体运动识别[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2058-2064.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021050798
数据类型 | 数目 |
---|---|
合计 | 21 144 |
无动作 | 15 152 |
开门1 | 348 |
开门2 | 370 |
关门1 | 327 |
关门2 | 352 |
打开冰箱 | 442 |
关闭冰箱 | 379 |
打开洗碗机 | 293 |
关闭洗碗机 | 266 |
打开抽屉1 | 204 |
关闭抽屉1 | 176 |
打开抽屉2 | 201 |
关闭抽屉2 | 163 |
打开抽屉3 | 242 |
关闭抽屉3 | 234 |
擦桌子 | 377 |
喝水 | 1 341 |
拨开关 | 277 |
Tab. 1 Data statistics
数据类型 | 数目 |
---|---|
合计 | 21 144 |
无动作 | 15 152 |
开门1 | 348 |
开门2 | 370 |
关门1 | 327 |
关门2 | 352 |
打开冰箱 | 442 |
关闭冰箱 | 379 |
打开洗碗机 | 293 |
关闭洗碗机 | 266 |
打开抽屉1 | 204 |
关闭抽屉1 | 176 |
打开抽屉2 | 201 |
关闭抽屉2 | 163 |
打开抽屉3 | 242 |
关闭抽屉3 | 234 |
擦桌子 | 377 |
喝水 | 1 341 |
拨开关 | 277 |
准确度 | 组合1 | 组合2 | 组合3 | |||
---|---|---|---|---|---|---|
操作1 | 操作2 | 操作1 | 操作2 | 操作1 | 操作2 | |
0.943 8 | 3×5-5×3 | 3×5-5×3 | 5×5-5×5 | 3×3-3×3 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 3×3-3×3 | |||||
0.943 2 | 3×5-5×3 | 3×3-3×3 | 5×5-5×5 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 2×6-6×2 | 5×5-5×5 | 3×3-3×3 | |||
0.942 6 | 1×3-3×1 | 3×3-3×3 | 5×5-5×5 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 2×6-6×2 | 5×5-5×5 | 3×3-3×3 | |||
0.942 0 | 3×5-5×3 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 1×7-7×1 | 5×5-5×5 | 3×3-3×3 | |||
0.941 5 | 3×5-5×3 | 5×5-5×5 | 3×5-5×3 | 5×5-5×5 | 3×5-5×3 | 5×5-5×5 |
3×5-5×3 | 1×7-7×1 | 3×5-5×3 | 5×5-5×5 |
Tab. 2 Topologies and accuracies of the top5 models
准确度 | 组合1 | 组合2 | 组合3 | |||
---|---|---|---|---|---|---|
操作1 | 操作2 | 操作1 | 操作2 | 操作1 | 操作2 | |
0.943 8 | 3×5-5×3 | 3×5-5×3 | 5×5-5×5 | 3×3-3×3 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 3×3-3×3 | |||||
0.943 2 | 3×5-5×3 | 3×3-3×3 | 5×5-5×5 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 2×6-6×2 | 5×5-5×5 | 3×3-3×3 | |||
0.942 6 | 1×3-3×1 | 3×3-3×3 | 5×5-5×5 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 2×6-6×2 | 5×5-5×5 | 3×3-3×3 | |||
0.942 0 | 3×5-5×3 | 5×5-5×5 | 5×5-5×5 | 3×3-3×3 | 5×5-5×5 | 3×3-3×3 |
5×5-5×5 | 1×7-7×1 | 5×5-5×5 | 3×3-3×3 | |||
0.941 5 | 3×5-5×3 | 5×5-5×5 | 3×5-5×3 | 5×5-5×5 | 3×5-5×3 | 5×5-5×5 |
3×5-5×3 | 1×7-7×1 | 3×5-5×3 | 5×5-5×5 |
编号 | 平均数 | 标准差 | 中位数 | 最小值 | 最大值 |
---|---|---|---|---|---|
1 | 0.919 522 | 0.005 528 | 0.920 868 | 0.902 169 | 0.927 428 |
2 | 0.920 135 | 0.006 067 | 0.922 891 | 0.900 702 | 0.925 996 |
3 | 0.924 570 | 0.004 411 | 0.926 382 | 0.906 374 | 0.929 054 |
4 | 0.901 550 | 0.026 100 | 0.908 650 | 0.797 012 | 0.921 621 |
5 | 0.917 748 | 0.009 842 | 0.920 067 | 0.866 850 | 0.924 272 |
6 | 0.920 104 | 0.005 025 | 0.922 175 | 0.901 746 | 0.925 013 |
7 | 0.919 362 | 0.006 375 | 0.921 375 | 0.897 056 | 0.924 669 |
8 | 0.919 620 | 0.005 268 | 0.921 226 | 0.895 362 | 0.924 658 |
9 | 0.919 226 | 0.009 095 | 0.922 838 | 0.880 309 | 0.924 767 |
10 | 0.922 278 | 0.004 002 | 0.922 978 | 0.903 349 | 0.927 468 |
11 | 0.919 174 | 0.014 942 | 0.924 615 | 0.862 486 | 0.926 538 |
12 | 0.920 456 | 0.004 927 | 0.921 955 | 0.903 347 | 0.927 389 |
13 | 0.926 368 | 0.005 139 | 0.928 377 | 0.909 028 | 0.930 858 |
14 | 0.920 531 | 0.012 185 | 0.924 319 | 0.864 361 | 0.928 656 |
15 | 0.922 617 | 0.004 133 | 0.924 509 | 0.909 749 | 0.926 209 |
16 | 0.923 063 | 0.002 901 | 0.923 541 | 0.908 954 | 0.925 753 |
17 | 0.902 136 | 0.021 373 | 0.909 083 | 0.832 309 | 0.925 316 |
18 | 0.926 029 | 0.004 275 | 0.927 353 | 0.911 680 | 0.930 610 |
19 | 0.922 370 | 0.007 389 | 0.924 482 | 0.893 722 | 0.926 957 |
20 | 0.921 048 | 0.002 975 | 0.921 664 | 0.910 051 | 0.925 131 |
Tab. 3 Statistics of F1 scores of the best 20 models
编号 | 平均数 | 标准差 | 中位数 | 最小值 | 最大值 |
---|---|---|---|---|---|
1 | 0.919 522 | 0.005 528 | 0.920 868 | 0.902 169 | 0.927 428 |
2 | 0.920 135 | 0.006 067 | 0.922 891 | 0.900 702 | 0.925 996 |
3 | 0.924 570 | 0.004 411 | 0.926 382 | 0.906 374 | 0.929 054 |
4 | 0.901 550 | 0.026 100 | 0.908 650 | 0.797 012 | 0.921 621 |
5 | 0.917 748 | 0.009 842 | 0.920 067 | 0.866 850 | 0.924 272 |
6 | 0.920 104 | 0.005 025 | 0.922 175 | 0.901 746 | 0.925 013 |
7 | 0.919 362 | 0.006 375 | 0.921 375 | 0.897 056 | 0.924 669 |
8 | 0.919 620 | 0.005 268 | 0.921 226 | 0.895 362 | 0.924 658 |
9 | 0.919 226 | 0.009 095 | 0.922 838 | 0.880 309 | 0.924 767 |
10 | 0.922 278 | 0.004 002 | 0.922 978 | 0.903 349 | 0.927 468 |
11 | 0.919 174 | 0.014 942 | 0.924 615 | 0.862 486 | 0.926 538 |
12 | 0.920 456 | 0.004 927 | 0.921 955 | 0.903 347 | 0.927 389 |
13 | 0.926 368 | 0.005 139 | 0.928 377 | 0.909 028 | 0.930 858 |
14 | 0.920 531 | 0.012 185 | 0.924 319 | 0.864 361 | 0.928 656 |
15 | 0.922 617 | 0.004 133 | 0.924 509 | 0.909 749 | 0.926 209 |
16 | 0.923 063 | 0.002 901 | 0.923 541 | 0.908 954 | 0.925 753 |
17 | 0.902 136 | 0.021 373 | 0.909 083 | 0.832 309 | 0.925 316 |
18 | 0.926 029 | 0.004 275 | 0.927 353 | 0.911 680 | 0.930 610 |
19 | 0.922 370 | 0.007 389 | 0.924 482 | 0.893 722 | 0.926 957 |
20 | 0.921 048 | 0.002 975 | 0.921 664 | 0.910 051 | 0.925 131 |
模型 | 测试集F1得分 |
---|---|
CNN[ | 0.851 0 |
BaselineCNN[ | 0.883 0 |
DeepConvLSTM[ | 0.915 0 |
b-LSTM-S[ | 0.927 0 |
EA-Single-best[ | 0.918 5 |
EA-Ensemble-best[ | 0.927 5 |
PNASNet-best | 0.930 8 |
Tab. 4 Comparison of optimal results of different models
模型 | 测试集F1得分 |
---|---|
CNN[ | 0.851 0 |
BaselineCNN[ | 0.883 0 |
DeepConvLSTM[ | 0.915 0 |
b-LSTM-S[ | 0.927 0 |
EA-Single-best[ | 0.918 5 |
EA-Ensemble-best[ | 0.927 5 |
PNASNet-best | 0.930 8 |
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