A survey of crowd counting and density estimation based on convolutional neural network

被引:76
作者
Fan, Zizhu [1 ]
Zhang, Hong [1 ]
Zhang, Zheng [2 ,4 ]
Lu, Guangming [2 ]
Zhang, Yudong [3 ]
Wang, Yaowei [4 ]
机构
[1] East China Jiaotong Univ, Sch Basic Sci, Nanchang 330013, Jiangxi, Peoples R China
[2] Harbin Inst Technol, Shenzhen 518055, Peoples R China
[3] Univ Leicester, Sch Informat, Leicester LE1 7RH, Leics, England
[4] Peng Cheng Lab, Shenzhen 518055, Peoples R China
关键词
Crowd counting; Crowd density estimation; Convolutional neural network; Deep learning; PARTIALLY OCCLUDED HUMANS; ANOMALY DETECTION; BAYESIAN COMBINATION; TIME; TRACKING; IMAGE; EVACUATION; BEHAVIORS; MULTIPLE; PEOPLE;
D O I
10.1016/j.neucom.2021.02.103
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Crowd counting and crowd density estimation methods are of great significance in the field of public security. Estimating crowd density and counting from single image or video frame has become an essential part of a computer vision system in various scenarios. In this paper, we comprehensively review the recent research advancement on crowd counting and density estimation. First of all, we introduce the background of crowd counting and crowd density estimation. Second, the traditional crowd counting methods are summarized. Third, we focus on reviewing the crowd counting and crowd density methods based on convolutional neural network (CNN) models. Next, we report and discuss the experimental results of a number of typical methods on benchmark datasets. Finally, we present the promising future directions of crowd counting and crowd density. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码:224 / 251
页数:28
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