Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2886-2892.DOI: 10.11772/j.issn.1001-9081.2023091269
• Multimedia computing and computer simulation • Previous Articles Next Articles
Zhiqiang ZHAO1,2(), Peihong MA1, Xinhong HEI1,2
Received:
2023-09-18
Revised:
2023-12-12
Accepted:
2023-12-15
Online:
2024-03-21
Published:
2024-09-10
Contact:
Zhiqiang ZHAO
About author:
MA Peihong, born in 1998, M. S. candidate. Her research interests include computer vision.Supported by:
通讯作者:
赵志强
作者简介:
马培红(1998—),女,河南郑州人,硕士研究生,主要研究方向:计算机视觉基金资助:
CLC Number:
Zhiqiang ZHAO, Peihong MA, Xinhong HEI. Crowd counting method based on dual attention mechanism[J]. Journal of Computer Applications, 2024, 44(9): 2886-2892.
赵志强, 马培红, 黑新宏. 基于双重注意力机制的人群计数方法[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2886-2892.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091269
数据集 | 样本数 | 标注点数 | 分辨率 | 人群规模 |
---|---|---|---|---|
ShanghaiTech | 1 198 | 330 165 | 768×1 024 | 9~578 |
UCF_CC_50 | 50 | 63 075 | 2 101×2 888 | 94~4 542 |
UCF-QNRF | 1 535 | >125×104 | 2 013×2 902 | 49~12 865 |
Tab. 1 Detail information of three datasets
数据集 | 样本数 | 标注点数 | 分辨率 | 人群规模 |
---|---|---|---|---|
ShanghaiTech | 1 198 | 330 165 | 768×1 024 | 9~578 |
UCF_CC_50 | 50 | 63 075 | 2 101×2 888 | 94~4 542 |
UCF-QNRF | 1 535 | >125×104 | 2 013×2 902 | 49~12 865 |
数据集 | 方法 | MAE | RMSE |
---|---|---|---|
ShanghaiTech | HA-CNN | 62.9 | 94.9 |
ADCrowdNet | 55.4 | 97.9 | |
PCC Net | 73.5 | 124.0 | |
LSC-CNN | 66.4 | 117.0 | |
EPA | 60.9 | 91.6 | |
Lw-Count | 69.7 | 100.5 | |
CG-DRCN | 60.2 | 94.0 | |
DA-DCCNN | 49.6 | 87.1 | |
UCF_CC_50 | ACSCP | 291.0 | 404.6 |
MRA-CNN | 240.8 | 352.6 | |
RANet | 239.8 | 319.4 | |
MBTTBF-SCFB | 233.1 | 300.9 | |
PCC Net | 240.0 | 315.5 | |
LSC-CNN | 225.6 | 302.7 | |
SDS-CNN | 229.4 | 325.6 | |
EPA | 250.1 | 342.7 | |
Lw-Count | 239.3 | 307.6 | |
HANet | 195.2 | 268.6 | |
DA-DCCNN | 165.3 | 227.7 | |
UCF-QNRF | RANet | 111.0 | 190.0 |
MBTTBF-SCFB | 97.5 | 165.2 | |
PCC Net | 246.4 | 247.1 | |
LSC-CNN | 120.5 | 218.2 | |
KDMG | 99.5 | 173.0 | |
SDS-CNN | 115.2 | 175.7 | |
Lw-Count | 149.7 | 238.4 | |
HANet | 99.1 | 159.2 | |
DA-DCCNN | 93.3 | 160.2 |
Tab. 2 Performance comparison on three datasets among different methods
数据集 | 方法 | MAE | RMSE |
---|---|---|---|
ShanghaiTech | HA-CNN | 62.9 | 94.9 |
ADCrowdNet | 55.4 | 97.9 | |
PCC Net | 73.5 | 124.0 | |
LSC-CNN | 66.4 | 117.0 | |
EPA | 60.9 | 91.6 | |
Lw-Count | 69.7 | 100.5 | |
CG-DRCN | 60.2 | 94.0 | |
DA-DCCNN | 49.6 | 87.1 | |
UCF_CC_50 | ACSCP | 291.0 | 404.6 |
MRA-CNN | 240.8 | 352.6 | |
RANet | 239.8 | 319.4 | |
MBTTBF-SCFB | 233.1 | 300.9 | |
PCC Net | 240.0 | 315.5 | |
LSC-CNN | 225.6 | 302.7 | |
SDS-CNN | 229.4 | 325.6 | |
EPA | 250.1 | 342.7 | |
Lw-Count | 239.3 | 307.6 | |
HANet | 195.2 | 268.6 | |
DA-DCCNN | 165.3 | 227.7 | |
UCF-QNRF | RANet | 111.0 | 190.0 |
MBTTBF-SCFB | 97.5 | 165.2 | |
PCC Net | 246.4 | 247.1 | |
LSC-CNN | 120.5 | 218.2 | |
KDMG | 99.5 | 173.0 | |
SDS-CNN | 115.2 | 175.7 | |
Lw-Count | 149.7 | 238.4 | |
HANet | 99.1 | 159.2 | |
DA-DCCNN | 93.3 | 160.2 |
组合序号 | 方法 | MAE | RMSE |
---|---|---|---|
① | VGG | 81.2 | 119.4 |
② | VGG+DCM | 60.7 | 95.4 |
③ | VGG+CAM | 79.0 | 114.9 |
④ | VGG+SAM | 75.3 | 109.7 |
⑤ | VGG+DAM | 69.1 | 98.1 |
⑥ | VGG+DCM+CAM | 58.3 | 92.1 |
⑦ | VGG+DCM+SAM | 53.2 | 90.8 |
⑧ | DA-DCCNN | 49.6 | 87.1 |
Tab. 3 Experimental results over combinations of various modules
组合序号 | 方法 | MAE | RMSE |
---|---|---|---|
① | VGG | 81.2 | 119.4 |
② | VGG+DCM | 60.7 | 95.4 |
③ | VGG+CAM | 79.0 | 114.9 |
④ | VGG+SAM | 75.3 | 109.7 |
⑤ | VGG+DAM | 69.1 | 98.1 |
⑥ | VGG+DCM+CAM | 58.3 | 92.1 |
⑦ | VGG+DCM+SAM | 53.2 | 90.8 |
⑧ | DA-DCCNN | 49.6 | 87.1 |
λ | MAE | RMSE | λ | MAE | RMSE |
---|---|---|---|---|---|
0 | 54.3 | 94.2 | 10-4 | 49.6 | 87.1 |
10-5 | 53.2 | 92.1 | 10-3 | 50.1 | 90.1 |
Tab. 4 Influence of different λ on performance of DA-DCCNN
λ | MAE | RMSE | λ | MAE | RMSE |
---|---|---|---|---|---|
0 | 54.3 | 94.2 | 10-4 | 49.6 | 87.1 |
10-5 | 53.2 | 92.1 | 10-3 | 50.1 | 90.1 |
σ | MAE | RMSE |
---|---|---|
0.001 | 73.4 | 112.6 |
0.010 | 56.4 | 96.2 |
0.100 | 52.8 | 90.1 |
0.200 | 49.6 | 87.1 |
0.300 | 51.3 | 90.7 |
Tab. 5 Influence of different σ on performance of DA-DCCNN
σ | MAE | RMSE |
---|---|---|
0.001 | 73.4 | 112.6 |
0.010 | 56.4 | 96.2 |
0.100 | 52.8 | 90.1 |
0.200 | 49.6 | 87.1 |
0.300 | 51.3 | 90.7 |
t | MAE | RMSE |
---|---|---|
0.000 1 | 54.6 | 97.3 |
0.001 0 | 49.6 | 87.1 |
0.005 0 | 53.8 | 89.5 |
0.010 0 | 57.7 | 96.4 |
0.100 0 | 58.3 | 95.6 |
Tab. 6 Influence of different t on performance of DA-DCCNN
t | MAE | RMSE |
---|---|---|
0.000 1 | 54.6 | 97.3 |
0.001 0 | 49.6 | 87.1 |
0.005 0 | 53.8 | 89.5 |
0.010 0 | 57.7 | 96.4 |
0.100 0 | 58.3 | 95.6 |
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