Smart City Gnosys

Smart city article details

Title	Scale-Context Perceptive Network For Crowd Counting And Localization In Smart City System
ID_Doc	47351
Authors	Zhai W.; Gao M.; Guo X.; Li Q.; Jeon G.
Year	2023
Published	IEEE Internet of Things Journal, 10, 21
DOI	http://dx.doi.org/10.1109/JIOT.2023.3268226
Abstract	The task of crowd counting and localization is to predict the count and position of people in a crowd, which is a practical and essential subtask in crowd analysis and smart city systems. However, the inherent problems of scale variation and background disturbance restrain their performance. While recent research focus on studying counting and localization independently, a few works are capable of executing both tasks simultaneously. To this end, we propose a scale-context perceptive network (SCPNet) to jointly tackle the crowd counting and localization tasks in a unified framework. Specifically, a scale perceptive (SP) module with a local-global branch schema is designed to capture multiscale information. Meanwhile, a context perceptive (CP) module, by the channel-spatial self-attention mechanism, is derived to suppress the background disturbance. Furthermore, a novel hierarchical scale loss function that combines the Euclidean loss function and structural similarity loss function is designed to prompt the proposed model to fulfill the counting and localization simultaneously. Extensive experiments on challenging crowd data sets prove the superiority of the proposed SCPNet compared with the state-of-the-art competitors in both objective and subjective evaluations. © 2023 IEEE.
Author Keywords	Convolutional neural network (CNN); crowd counting; crowd localization; self-attention mechanism; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
16658	0.907	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	0.867	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)
7671	0.866	Zhai W.; Gao M.; Souri A.; Li Q.; Guo X.; Shang J.; Zou G.	An Attentive Hierarchy Convnet For Crowd Counting In Smart City	Cluster Computing, 26, 2 (2023)
16357	0.865	Solmaz G.; Baranwal P.; Cirillo F.	Countmein: Adaptive Crowd Estimation With Wi-Fi In Smart Cities	2022 IEEE International Conference on Pervasive Computing and Communications, PerCom 2022 (2022)
16111	0.864	Ilyas N.; Shahzad A.; Kim K.	Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, And Performance Evaluation	Sensors (Switzerland), 20, 1 (2020)
4875	0.862	Avvenuti M.; Bongiovanni M.; Ciampi L.; Falchi F.; Gennaro C.; Messina N.	A Spatio- Temporal Attentive Network For Video-Based Crowd Counting	Proceedings - IEEE Symposium on Computers and Communications, 2022-June (2022)
11921	0.86	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)
16685	0.859	Hu Y.; Liu Y.; Cao G.; Wang J.	Crowdcl: Unsupervised Crowd Counting Network Via Contrastive Learning	IEEE Internet of Things Journal, 12, 12 (2025)
50724	0.853	De Cock L.; Verstockt S.; Vandeviver C.; Van de Weghe N.	Smart Crowd Management: The Data, The Users And The Solution	Leibniz International Proceedings in Informatics, LIPIcs, 240 (2022)
38647	0.851	Guo X.; Gao M.; Zhai W.; Li Q.; Pan J.; Zou G.	Multiscale Aggregation Network Via Smooth Inverse Map For Crowd Counting	Multimedia Tools and Applications, 83, 22 (2024)