NDLI: Crowd counting by the dual-branch scale-aware network with ranking loss constraints

Content Provider	IET Digital Library
Author	Wu, Qin Yan, Fangfang Chai, Zhilei Guo, Guodong
Abstract	Image crowd counting is a challenging problem. This study proposes a new deep learning method that estimates crowd counting for the congested scene. The proposed network is composed of two major components: the first ten layers of VGG16 are used as the backbone network, and a dual-branch (named as Branch_S and Branch_D) network is proposed to be the second part of the network. Branch_S extracts low-level information (head blob) through a shallow fully convolutional network and Branch_D uses a deep fully convolutional network to extract high-level context features (faces and body). Features learnt from the two different branches can handle the problem of scale variation due to perspective effects and image size differences. Features of different scales extracted from the two branches are fused to generate predicted density map. On the basis of the fact that an original graph must contain more or equal number of persons than any of its sub-images, a ranking loss function utilising the constraint relationship inside an image is proposed. Moreover, the ranking loss is combined with Euclidean loss as the final loss function. Our approach is evaluated on three benchmark datasets, and better results are achieved compared with the state-of-the-art works.
Starting Page	101
Ending Page	109
Page Count	9
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 3, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0704
Journal	IET Computer Vision
Publisher Date	2020-01-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Branch_D Branch_S Combinatorial Mathematics Computer Vision And Image Processing Technique Congested Scene Convolutional Neural Nets Deep Fully Convolutional Network Deep Learning Method Density Map Dual-branch Scale-aware Network Euclidean Loss Feature Extraction Graph Theory High-level Context Feature Image Crowd Counting Image Recognition Image Size Differences Knowledge Engineering Technique Learning in AI Neural Computing Technique Ranking Loss Constraint Ranking Loss Function Shallow Fully Convolutional Network VGG16
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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