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المؤلفKhan, Muhammad Asif
المؤلفMenouar, Hamid
المؤلفHamila, Ridha
تاريخ الإتاحة2023-04-04T09:09:09Z
تاريخ النشر2023
اسم المنشورJournal of Real-Time Image Processing
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s11554-023-01286-8
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41636
الملخصAutomatic 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).
راعي المشروعThis 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.
اللغةen
الناشرInstitute for Ionics
الموضوعCNN
Crowd counting
Density estimation
Lightweight
Real-time
العنوانLCDnet: a lightweight crowd density estimation model for real-time video surveillance
النوعArticle
رقم العدد2
رقم المجلد20
dc.accessType Open Access


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