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Dilated Convolution-based Feature Refinement Network for Crowd Localization

文献类型：期刊论文

作者Gao, Xingyu7; Xie, Jinyang6; Chen, Zhenyu4,5; Liu, An-An3; Sun, Zhenan1,2; Lyu, Lei6
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2023-11-01
卷号19期号:6页码:16
关键词Dilated convolution Feature Refinement crowd localization contextual information
ISSN号1551-6857
DOI10.1145/3571134
通讯作者Xie, Jinyang(xiejinyangsdnu@163.com) ; Lyu, Lei(lvlei@sdnu.edu.cn)
英文摘要As an emerging computer vision task, crowd localization has received increasing attention due to its ability to produce more accurate spatially predictions. However, continuous scale variations in complex crowd scenes lead to tiny individuals at the edges, so that existing methods cannot achieve precise crowd localization. Aiming at alleviating the above problems, we propose a novel Dilated Convolution-based Feature Refinement Network (DFRNet) to enhance the representation learning capability. Specifically, the DFRNet is built with three branches that can capture the information of each individual in crowd scenes more precisely. More specifically, we introduce a Feature Perception Module to model long-range contextual information at different scales by adopting multiple dilated convolutions, thus providing sufficient feature information to perceive tiny individuals at the edge of images. Afterwards, a Feature Refinement Module is deployed at multiple stages of the three branches to facilitate the mutual refinement of feature information at different scales, thus further improving the expression capability of multi-scale contextual information. By incorporating the above modules, DFRNet can locate individuals in complex scenes more precisely. Extensive experiments on multiple datasets demonstrate that the proposed method has more advanced performance compared to existing methods and can be more accurately adapted to complex crowd scenes.
WOS关键词MEAN SQUARED ERROR
资助项目National Natural Science Foundation of China[61976127] ; Science and Technology Innovation 2030-Major Project (Brain Science and Brain-Like Intelligence Technology)[2022ZD0208700]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001035785200039
出版者ASSOC COMPUTING MACHINERY
资助机构National Natural Science Foundation of China ; Science and Technology Innovation 2030-Major Project (Brain Science and Brain-Like Intelligence Technology)
源URL[http://ir.ia.ac.cn/handle/173211/54002]  
专题多模态人工智能系统全国重点实验室
通讯作者Xie, Jinyang; Lyu, Lei
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China
4.China Elect Power Res Inst, Beijing, Peoples R China
5.State Grid Corp China, Big Data Ctr, Beijing, Peoples R China
6.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China
7.Chinese Acad Sci, Inst Microelect, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Xingyu,Xie, Jinyang,Chen, Zhenyu,et al. Dilated Convolution-based Feature Refinement Network for Crowd Localization[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2023,19(6):16.
APA Gao, Xingyu,Xie, Jinyang,Chen, Zhenyu,Liu, An-An,Sun, Zhenan,&Lyu, Lei.(2023).Dilated Convolution-based Feature Refinement Network for Crowd Localization.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,19(6),16.
MLA Gao, Xingyu,et al."Dilated Convolution-based Feature Refinement Network for Crowd Localization".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 19.6(2023):16.

入库方式： OAI收割

来源：自动化研究所

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