NDLI: Dual attention module and multi-label based fully convolutional network for crowd counting

Content Provider	IET Digital Library
Author	Wang, Suyu Yang, Bin Liu, Bo Zheng, Guanghui
Abstract	High-density crowd counting in natural scenes is an extremely difficult and challenging research subject in computer vision. Although the algorithm based on the convolutional neural network has achieved significantly better results than the traditional algorithm, most of them tend to focus on the local features of images, and difficult to obtain the rich global contextual dependencies. To solve this problem, a dual attention module and a multi-label based fully convolutional network are proposed in this study. Moreover, the authors improve the algorithm by the following multiple perspectives. Firstly, introducing the dual attention module, the global-context and long-range dependency are adaptively integrated into both spatial and channel dimensions, which improve the network expression ability. Then, the prediction error is effectively reduced by designing a multi-label mechanism, so the crowd-counting task is transformed into foreground and background segmentation tasks to assist in the regression task of the density map. Furthermore, on the basis of the traditional Euclidean distance loss and cross-entropy loss, the structural similarity index is introduced to further improve the training effect of the model. The test results of the UCF_CC_50, ShanghaiTech, and UCF-QNRF datasets indicate that the proposed method is superior to the current mainstream algorithm.
Starting Page	443
Ending Page	451
Page Count	9
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 7, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0674
Journal	IET Computer Vision
Publisher Date	2020-02-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Segmentation Task Channel Dimension Computer Vision Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Cross-entropy Loss Crowd-counting Task Current Mainstream Algorithm Density Map Dual Attention Module Entropy Euclidean Distance Loss Foreground Segmentation Task High-density Crowd Counting Image Segmentation Multilabel Based Fully Convolutional Network Multilabel Mechanism Natural Scenes Network Expression Ability Neural Computing Technique Object Detection Optical, Image And Video Signal Processing Rich Global Contextual Dependencies Spatial Dimension Structural Similarity Index
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

Crowd counting using a self-attention multi-scale cascaded network

Foreground segmentation using multiscale convolutional neural network

Multi-scale supervised network for crowd counting

Crowd counting with segmentation attention convolutional neural network

Deep convolution network for dense crowd counting

Background segmentation of dynamic scenes based on dual model

Object counting method based on dual attention network

Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and GrabCut

Dual attention module and multi-label based fully convolutional network for crowd counting

Similar Documents

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

Crowd counting using a self-attention multi-scale cascaded network

Foreground segmentation using multiscale convolutional neural network

Multi-scale supervised network for crowd counting

Crowd counting with segmentation attention convolutional neural network

Deep convolution network for dense crowd counting

Background segmentation of dynamic scenes based on dual model

Object counting method based on dual attention network

Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and GrabCut

Dual attention module and multi-label based fully convolutional network for crowd counting