Smart City Gnosys

Smart city article details

Title	An Attentive Hierarchy Convnet For Crowd Counting In Smart City
ID_Doc	7671
Authors	Zhai W.; Gao M.; Souri A.; Li Q.; Guo X.; Shang J.; Zou G.
Year	2023
Published	Cluster Computing, 26, 2
DOI	http://dx.doi.org/10.1007/s10586-022-03749-2
Abstract	Crowd counting plays a crucial rule in the development of smart city. However, the problems of scale variations and background interferences degrade the performance of the crowd counting in real-world scenarios. To address these problems, a novel attentive hierarchy ConvNet (AHNet) is proposed in this paper. The AHNet extracts hierarchy features by a designed discriminative feature extractor and mines the semantic features in a coarse-to-fine manner by a hierarchical fusion strategy. Meanwhile, a re-calibrated attention (RA) module is built in various levels to suppress the influence of background interferences, and a feature enhancement (FE) module is built to recognize head regions at various scales. Experimental results on five people crowd datasets and two cross-domain vehicle crowd datasets illustrate that the proposed AHNet achieves competitive performance in accuracy and generalization. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Author Keywords	Attention mechanism; Crowd counting; Hierarchical strategy; Smart city

Similar Articles

Id	Similarity	Authors	Title	Published
16658	0.884	Guo X.; Song K.; Gao M.; Zhai W.; Li Q.; Jeon G.	Crowd Counting In Smart City Via Lightweight Ghost Attention Pyramid Network	Future Generation Computer Systems, 147 (2023)
16657	0.874	Dong L.; Zhang H.; Yang K.; Zhou D.; Shi J.; Ma J.	Crowd Counting By Using Top-K Relations: A Mixed Ground-Truth Cnn Framework	IEEE Transactions on Consumer Electronics, 68, 3 (2022)
47351	0.866	Zhai W.; Gao M.; Guo X.; Li Q.; Jeon G.	Scale-Context Perceptive Network For Crowd Counting And Localization In Smart City System	IEEE Internet of Things Journal, 10, 21 (2023)
16111	0.858	Ilyas N.; Shahzad A.; Kim K.	Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, And Performance Evaluation	Sensors (Switzerland), 20, 1 (2020)
11921	0.853	Xie Z.; Shao F.; Mu B.; Chen H.; Jiang Q.; Lu C.; Ho Y.-S.	Bgdfnet: Bidirectional Gated And Dynamic Fusion Network For Rgb-T Crowd Counting In Smart City System	IEEE Transactions on Instrumentation and Measurement, 73 (2024)
17778	0.853	Mansouri W.; Alohali M.A.; Alqahtani H.; Alruwais N.; Alshammeri M.; Mahmud A.	Deep Convolutional Neural Network-Based Enhanced Crowd Density Monitoring For Intelligent Urban Planning On Smart Cities	Scientific Reports, 15, 1 (2025)
38001	0.851	Hu J.; Feng M.; Liu F.; Chen Y.	Mpanet: A Multi-Stage Pixel-Level Attention Network For Crowd Counting	Procedia Computer Science, 208 (2022)
16685	0.851	Hu Y.; Liu Y.; Cao G.; Wang J.	Crowdcl: Unsupervised Crowd Counting Network Via Contrastive Learning	IEEE Internet of Things Journal, 12, 12 (2025)