Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

doi:10.1109/TNNLS.2023.3336774

	Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
	Gao, Jiaqi 1; Huang, Zhizhong 1; Lei, Yiming 1; Shan, Hongming 2,3,4; Wang, James Z.5; Wang, Fei-Yue6,7,8 ; Zhang, Junping 1
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2023-12-13
页码	14
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn) ; Zhang, Junping(jpzhang@fudan.edu.cn)
摘要	Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods.
关键词	Crowd counting feature pyramid ranking semi-supervised learning
DOI	10.1109/TNNLS.2023.3336774
关键词[WOS]	PEDESTRIAN DETECTION ; NETWORK
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001127680600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55010
专题	多模态人工智能系统全国重点实验室
通讯作者	Wang, Fei-Yue; Zhang, Junping
作者单位	1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China 2.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China 3.Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China 4.Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China 5.Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 7.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 8.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Gao, Jiaqi,Huang, Zhizhong,Lei, Yiming,et al. Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:14.
APA	Gao, Jiaqi.,Huang, Zhizhong.,Lei, Yiming.,Shan, Hongming.,Wang, James Z..,...&Zhang, Junping.(2023).Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA	Gao, Jiaqi,et al."Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):14.