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AuthorKhan, Muhammad Asif
AuthorMenouar, Hamid
AuthorHamila, Ridha
Available date2023-04-04T09:09:09Z
Publication Date2023
Publication NameJournal of Real-Time Image Processing
ResourceScopus
URIhttp://dx.doi.org/10.1007/s11554-023-01286-8
URIhttp://hdl.handle.net/10576/41636
AbstractAutomatic crowd counting using density estimation has gained significant attention in computer vision research. As a result, a large number of crowd counting and density estimation models using convolution neural networks (CNN) have been published in the last few years. These models have achieved good accuracy over benchmark datasets. However, attempts to improve the accuracy often lead to higher complexity in these models. In real-time video surveillance applications using drones with limited computing resources, deep models incur intolerable higher inference delay. In this paper, we propose (i) a Lightweight Crowd Density estimation model (LCDnet) for real-time video surveillance, and (ii) an improved training method using curriculum learning (CL). LCDnet is trained using CL and evaluated over two benchmark datasets i.e., DroneRGBT and CARPK. Results are compared with existing crowd models. Our evaluation shows that the LCDnet achieves a reasonably good accuracy while significantly reducing the inference time and memory requirement and thus can be deployed over edge devices with very limited computing resources. 2023, The Author(s).
SponsorThis publication was made possible by the PDRA award PDRA7-0606-21012 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.
Languageen
PublisherInstitute for Ionics
SubjectCNN
Crowd counting
Density estimation
Lightweight
Real-time
TitleLCDnet: a lightweight crowd density estimation model for real-time video surveillance
TypeArticle
Issue Number2
Volume Number20
dc.accessType Open Access


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