CASIA OpenIR
Depth Information Guided Crowd Counting for complex crowd scenes
Xu, Mingliang1; Ge, Zhaoyang1; Jiang, Xiaoheng1; Cui, Gaoge1; Lv, Pei1; Zhou, Bing1; Xu, Changsheng2
Source PublicationPATTERN RECOGNITION LETTERS
ISSN0167-8655
2019-07-01
Volume125Pages:563-569
Corresponding AuthorJiang, Xiaoheng(jiangxiaoheng@zzu.edu.cn)
AbstractIt is important to monitor and analyze crowd events for the sake of city safety. In an EDOF (extended depth of field) image with a crowded scene, the distribution of people is highly imbalanced. People far away from the camera look much smaller and often occlude each other heavily, while people close to the camera look larger. In such a case, it is difficult to accurately estimate the number of people by using one technique. In this paper, we propose a Depth Information Guided Crowd Counting (DigCrowd) method to deal with crowded EDOF scenes. DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region. Then Digcrowd maps the far-view region to its crowd density map and uses a detection method to count the people in the near-view region. In addition, we introduce a new crowd dataset that contains 10 0 0 images. Experimental results demonstrate the effectiveness of our DigCrowd method. (C) 2019 Elsevier B.V. All rights reserved.
KeywordCrowd counting Depth information Pedestrian detection Density estimation
DOI10.1016/j.patrec.2019.02.026
WOS KeywordPEDESTRIAN DETECTION ; CLASSIFICATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61802351] ; National Natural Science Foundation of China[61822701] ; National Natural Science Foundation of China[61672469] ; National Natural Science Foundation of China[61602420] ; China Postdoctoral Science Foundation[2018M632802]
Funding OrganizationNational Natural Science Foundation of China ; China Postdoctoral Science Foundation
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000482374500077
PublisherELSEVIER
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27318
Collection中国科学院自动化研究所
Corresponding AuthorJiang, Xiaoheng
Affiliation1.Zhengzhou Univ, 100 Sci Ave, Zhengzhou 450000, Henan, Peoples R China
2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Mingliang,Ge, Zhaoyang,Jiang, Xiaoheng,et al. Depth Information Guided Crowd Counting for complex crowd scenes[J]. PATTERN RECOGNITION LETTERS,2019,125:563-569.
APA Xu, Mingliang.,Ge, Zhaoyang.,Jiang, Xiaoheng.,Cui, Gaoge.,Lv, Pei.,...&Xu, Changsheng.(2019).Depth Information Guided Crowd Counting for complex crowd scenes.PATTERN RECOGNITION LETTERS,125,563-569.
MLA Xu, Mingliang,et al."Depth Information Guided Crowd Counting for complex crowd scenes".PATTERN RECOGNITION LETTERS 125(2019):563-569.
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