Density-Aware Multi-Task Learning for Crowd Counting

doi:10.1109/TMM.2020.2980945

	Density-Aware Multi-Task Learning for Crowd Counting
	Jiang, Xiaoheng 1; Zhang, Li 1; Zhang, Tianzhu 2; Lv, Pei 1; Zhou, Bing 1; Pang, Yanwei 3; Xu, Mingliang 1; Xu, Changsheng4
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2021
卷号	23 页码:443-453
通讯作者	Xu, Mingliang(iexumingliang@zzu.edu.cn)
摘要	In this paper, we present a method called density-aware convolutional neural network (DensityCNN) to perform the crowd counting task in various crowded scenes. The key idea of the DensityCNN is to utilize high-level semantic information to provide guidance and constraint when generating density maps. To this end, we implement the DensityCNN by adopting a multi-task CNN structure to jointly learn density-level classification and density map estimation. The density-level classification task learns multi-channel semantic features that are aware of the density distributions of the input image. This task is accomplished via our specially designed group-based convolutional structure in a supervised learning manner. In the density map estimation task, these semantic features are deployed together with high-dimension convolutional features to generate density maps with lower count errors. Extensive experiments on four challenging crowd datasets (ShanghaiTech, UCF_CC_50, UCF-QNCF, and WorldExpo'10) and one vehicle dataset TRANCOS demonstrate the effectiveness of the proposed method.
关键词	Task analysis Semantics Estimation Feature extraction Convolutional neural networks Cameras Head Convolutional neural network crowd counting density-level classification density map estimation multi-task learning
DOI	10.1109/TMM.2020.2980945
关键词[WOS]	DEEP
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61802351] ; National Natural Science Foundation of China[61822701] ; National Natural Science Foundation of China[61872324] ; National Natural Science Foundation of China[61772474] ; China Postdoctoral Science Foundation[2018M632802] ; Key R&D and Promotion Projects in Henan Province[192102310258]
项目资助者	National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Key R&D and Promotion Projects in Henan Province
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000601877600035
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：45[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42827
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Mingliang
作者单位	1.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Xiaoheng,Zhang, Li,Zhang, Tianzhu,et al. Density-Aware Multi-Task Learning for Crowd Counting[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:443-453.
APA	Jiang, Xiaoheng.,Zhang, Li.,Zhang, Tianzhu.,Lv, Pei.,Zhou, Bing.,...&Xu, Changsheng.(2021).Density-Aware Multi-Task Learning for Crowd Counting.IEEE TRANSACTIONS ON MULTIMEDIA,23,443-453.
MLA	Jiang, Xiaoheng,et al."Density-Aware Multi-Task Learning for Crowd Counting".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):443-453.