Full Text:   <183>

Summary:  <21>

CLC number: TP391.4

On-line Access: 2020-08-10

Received: 2019-06-05

Revision Accepted: 2020-02-06

Crosschecked: 2020-06-11

Cited: 0

Clicked: 311

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jie-hao Huang

https://orcid.org/0000-0003-1412-1324

Xiao-guang Di

https://orcid.org/0000-0002-5709-6862

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.8 P.1150-1160

http://doi.org/10.1631/FITEE.1900282


A novel convolutional neural network method for crowd counting


Author(s):  Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen

Affiliation(s):  Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China

Corresponding email(s):   18s004055@hit.edu.cn, dixiaoguang@hit.edu.cn

Key Words:  Crowd counting, Density estimation, Segmentation prior map, Uniform function


Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen. A novel convolutional neural network method for crowd counting[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1150-1160.

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Abstract: 
Crowd density estimation, in general, is a challenging task due to the large variation of head sizes in the crowds. Existing methods always use a multi-column convolutional neural network (MCNN) to adapt to this variation, which results in an average effect in areas with different densities and brings a lot of noise to the density map. To address this problem, we propose a new method called the segmentation-aware prior network (SAPNet), which generates a high-quality density map without noise based on a coarse head-segmentation map. SAPNet is composed of two networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression convolutional neural network (CR-CNN) as the back end. With only the single dot annotation, we generate the ground truth of segmentation masks in heads. Then, based on the ground truth, FS-CNN outputs a coarse head-segmentation map, which helps eliminate the noise in regions without people in the density map. By inputting the head-segmentation map generated by the front end, CR-CNN performs accurate crowd counting estimation and generates a high-quality density map. We demonstrate SAPNet on four datasets (i.e., ShanghaiTech, UCF-CC-50, WorldExpo’10, and UCSD), and show the state-of-the-art performances on ShanghaiTech part B and UCF-CC-50 datasets.

一种新的基于卷积神经网络的人群计数方法

黄杰浩,遆晓光,吴俊德,陈瑷玥
哈尔滨工业大学控制与仿真中心,中国哈尔滨市,150080

摘要:人群密度估计是一项具有挑战性的任务,因为人群中人头大小存在大范围变化。现有方法均采用多列式结构卷积神经网络去适应这种变化,但会导致密度图上不同密度区域产生平均效应并引入额外噪声。为解决该问题,提出一种新的基于分割先验图的神经网络方法,在分割图基础上生成一个高质量且没有噪声的密度图。该网络主要包括两个部分,即头部的人群前景分割神经网络和尾部的人群回归神经网络。在数据集只提供单点人头标记的情况下,采用均匀函数生成人群头部的掩膜真值图。基于该真值图,前景分割网络输出人群分割图,可有效减少密度图中无人区域噪声。将人群分割图输入人群回归网络,后者能生成高质量人群密度图并提供准确的人数估计。在4个公开数据集(即ShanghaiTech、UCF-CC-50、WorldExpo’10和UCSD)上验证了所提方法有效性;其中,在ShanghaiTech partB和UCF-CC-50两个数据集上该方法取得了当前最好结果。

关键词:人群计数;密度估计;分割先验图;均匀函数

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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