NDLI: Object counting method based on dual attention network

Content Provider	IET Digital Library
Author	Zhang, Shihui Li, He Kong, Weihang
Abstract	The challenging problem that the authors solved in this study is to precisely estimate the number of objects in an image. Combining the spatial attention mechanism and pyramid structure, a novel atrous pyramid attention module is introduced to extract precise dense multi-scale features for object counting. Also, a global attention feature module is designed to enhance the ability of the network to learn feature representation based on channel attention mechanism. Combining the proposed atrous pyramid attention module and global attention feature module, a novel object counting method based on a dual attention network is established in this study. The experiments on public vehicle counting dataset including TRANCOS and crowd counting dataset including Mall and Shanghitech_A datasets demonstrate the proposed method achieves competitive performance, and the ablation study verifies the structure rationality of the designed modules.
Starting Page	1621
Ending Page	1627
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0465
Journal	IET Image Processing
Publisher Date	2020-03-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atrous Pyramid Attention Module Channel Attention Mechanism Computer Vision And Image Processing Technique Designed Module Dual Attention Network Feature Extraction Feature Representation Global Attention Feature Module Image Processing Image Representation Knowledge Engineering Technique Learning in AI Object Counting Method Optical, Image And Video Signal Processing Precise Dense Multiscale Feature Pyramid Structure Regression Analysis Spatial Attention Mechanism Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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