Cross-Level Parallel Network for Crowd Counting

doi:10.1109/TII.2019.2935244

	Cross-Level Parallel Network for Crowd Counting
	Li, Jing 1; Xue, Yaokai 1; Wang, Weiqun2 ; Ouyang, Gaoxiang 3,4
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
	2020
卷号	16 期号:1 页码:566-576
通讯作者	Ouyang, Gaoxiang(ouyang@bnu.edu.cn)
摘要	Automated people counting in crowd scenes is challenging due to large variations in scale, density, and background clutter. To tackle them, we propose a novel cross-level parallel network (CLPNet) by extracting multiple low-level features from VGG16 and fusing them with specific scale aggregation modules in the high-level stage. To deal with scale variation, we design five different aggregation modules for multiscale fusion. Furthermore, the ground truth is processed skillfully to eliminate the mismatches caused by the scale variation between heads and density maps. To cope with background clutter, cross-level feature fusion is implemented. Higher-level semantic information could effectively separate head from background and regain the lost low-level detailed information. To address the variation of density, we design a parallel network, in which two separate channels focus on different density-level estimation, and attain more accurate counting results. Finally, we evaluate the proposed CLPNet on four representative crowd counting datasets, i.e., ShanghaiTech, UCF_CC_50, WorldExpo'10, and UCF_QNRF. The experimental results demonstrate that with the cross-level and multiscale structure CLPNet achieves superior performance compared with the state-of-the-art crowd counting methods.
关键词	Convolutional neural network (CNN) cross-level and multiscale features crowd counting density map scale aggregation network
DOI	10.1109/TII.2019.2935244
关键词[WOS]	SCALE ; IMAGE
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFC0820205] ; National Natural Science Foundation of China[61703198] ; Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province[2018ACB21014] ; Open Fund of State Key Laboratory of Management and Control for Complex Systems[20180109] ; National Key Research and Development Program of China[2017YFC0820205] ; National Natural Science Foundation of China[61703198] ; Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province[2018ACB21014] ; Open Fund of State Key Laboratory of Management and Control for Complex Systems[20180109]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province ; Open Fund of State Key Laboratory of Management and Control for Complex Systems
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:000508428900054
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：24[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/173211/29544
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Ouyang, Gaoxiang
作者单位	1.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 4.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Li, Jing,Xue, Yaokai,Wang, Weiqun,et al. Cross-Level Parallel Network for Crowd Counting[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(1):566-576.
APA	Li, Jing,Xue, Yaokai,Wang, Weiqun,&Ouyang, Gaoxiang.(2020).Cross-Level Parallel Network for Crowd Counting.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16(1),566-576.
MLA	Li, Jing,et al."Cross-Level Parallel Network for Crowd Counting".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 16.1(2020):566-576.