NDLI: Deep convolution network for dense crowd counting

Content Provider	IET Digital Library
Author	Zhang, Wei Wang, Yongjie Liu, Yanyan Zhu, Jianghua
Abstract	Estimating the total number of people in a crowded situation is a challenging task due to numerous occlusions and perspective changes existing in crowd images. To address this issue, the authors have proposed a new deep learning framework for accurate and efficient crowd counting here. Inspired by multi-column convolutional neural network (MCNN) and contextual pyramid convolutional neural network (CP-CNN), the authors use a combination of a two branches, convolutional neutral network (CNN) and transposed convolutional layers, to generate a high-quality density map. The two-branch CNN for feature extraction generates a density map that is only a quarter of size of the original image Then a set of transposed convolutional layers and convolutional layers are combined with the network to make up for the detail loss of the density map conducted by stacked pooling. Compared with MCNN and CP-CNN, the authors’ approach employs fewer branches and simpler architecture. Experimental result shows that their approach achieves MAE 80.7 and MSE 131.2 in ShanghaiTech PartA dataset, MAE 15.6 and MSE 26.8 in ShanghaiTech PartB dataset, and MAE Average 7.1 in WorldExpo'10 dataset.
Starting Page	621
Ending Page	627
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0435
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neutral Network CP-CNN Deep Convolution Network Deep Learning Framework Dense Crowd Counting Density Map Feature Extraction High-quality Density Map Image Recognition Learning in AI MCNN Neural Computing Technique Object Detection ShanghaiTech PartA Dataset ShanghaiTech PartB Dataset Stacked Pooling Transposed Convolutional Layer Two-branch CNN
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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