.png){kind=icon}
返回
Multiscale aggregation network via smooth inverse map for crowd counting
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-13664-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Crowd counting is a practical yet essential research topic in computer vision, which has been beneficial to diverse applications in smart city environment safety. The commonly adopted paradigm in most existing methods is to regress a Gaussian density map that works as the learning objective during model training. However, given the unavoidable identity occlusion and scale variation in a crowd image, the corresponding Gaussian density map is degraded, failing to provide reliable supervision for optimization. To address this problem, we propose to replace the traditional Gaussian density map with a better alternation, namely the smooth inverse map (SIM). The proposed SIM can reflect the head location spatially and provide a smooth gradient to stabilize the model learning. Besides, we want the method to learn more discriminative features to cope with the challenge of large-scale variations. We deliver a multiscale aggregation (MA) to adaptively fuse features in different hierarchies to benefit semantic information under diverse receptive filed. The SIM and MA are meant to be complementary modules to guide the model in learning an accurate density map. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art techniques.
Keyword:
Crowd counting
Smart city
Scale variation
Density map
Convolutional neural network
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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Population Structure of an Invasive Parthenogenetic Gastropod in Coastal Lakes and Estuaries of Northern KwaZulu-Natal, South AfricaPLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏分享收藏分享收藏分享收藏
查看更多内容