.png){kind=icon}
返回
SC2Net: Scale-aware Crowd Counting Network with Pyramid Dilated Convolution
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-022-03648-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate crowd counting is still challenging due to the variations of crowd heads. Most of crowd counting methods adopt multi-branch networks to extract multi-scale information. However, these networks are too complex to be optimized. To solve these problems, we propose an efficient scale-aware crowd counting network named SC2Net, which adopts the encoder-decoder framework. The encoder uses the first ten layers of VGG16 to extract the primary feature information. The decoder is mainly consisted of our proposed residual pyramid dilated convolution (ResPyDConv) modules to regress predicted density maps. Specifically, the ResPyDConv module is composed of pyramid dilated convolution (PyDConv). Each PyDConv adopts dilated convolutions with different dilated rates. PyDConv divides feature maps into different groups and extracts multi-scale feature information. Extensive experiments are conducted on ShanghaiTech, UCF_CC_50, UCF_QNRF, and NWPU_Crowd datasets. Qualitative and quantitive results show the superiority of our proposed network to the other state-of-the-art methods.
Keyword:
Crowd counting
Crowd localization
Multi-scale feature learning
Residual network
Pyramid convolution
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Measurements of fatty acid synthesis by incorporation of deuterium from deuterated water.png)
Biochemistry
BiochemistryIF0
PREAI
分享收藏
查看更多内容