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AuthorKhan, Muhammad Asif
AuthorMenouar, Hamid
AuthorHamila, Ridha
Available date2024-08-21T09:49:58Z
Publication Date2023
Publication NameProceedings - IEEE Consumer Communications and Networking Conference, CCNC
ResourceScopus
ISSN23319860
URIhttp://dx.doi.org/10.1109/CCNC51644.2023.10059904
URIhttp://hdl.handle.net/10576/57848
AbstractVideo surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd density (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed for automatic crowd counting and density estimation using images and videos. However, the performance and accuracy of such models typically depends upon the model architecture i.e., deeper CNN models improve accuracy at the cost of increased inference time. In this paper, we propose a novel crowd density estimation model for drones (DroneNet) using Self-organized Operational Neural Networks (Self-ONN). Self-ONN provides efficient learning capabilities with lower computational complexity as compared to CNN-based models. We tested our algorithm on two drone-view public datasets. Our evaluation shows that the proposed DroneNet shows superior performance on an equivalent CNN-based model.
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.
Languageen
PublisherIEEE
SubjectCNN
crowd counting
density estimation
drones
self-ONNs
TitleDroneNet: Crowd Density Estimation using Self-ONNs for Drones
TypeConference
Pagination455-460
Volume Number2023-January
dc.accessType Full Text


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