LCDnet: a lightweight crowd density estimation model for real-time video surveillance
<p dir="ltr">Automatic 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....
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| _version_ | 1864513530567327744 |
|---|---|
| author | Muhammad Asif Khan (7367468) |
| author2 | Hamid Menouar (16904844) Ridha Hamila (7006457) |
| author2_role | author author |
| author_facet | Muhammad Asif Khan (7367468) Hamid Menouar (16904844) Ridha Hamila (7006457) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Asif Khan (7367468) Hamid Menouar (16904844) Ridha Hamila (7006457) |
| dc.date.none.fl_str_mv | 2023-03-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11554-023-01286-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/LCDnet_a_lightweight_crowd_density_estimation_model_for_real-time_video_surveillance/24998270 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Machine learning Crowd counting CNN Density estimation Lightweight Real-time |
| dc.title.none.fl_str_mv | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Automatic 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Real-Time Image Processing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11554-023-01286-8" target="_blank">https://dx.doi.org/10.1007/s11554-023-01286-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4dae97b164691427429eeedc9cba0aad |
| identifier_str_mv | 10.1007/s11554-023-01286-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24998270 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LCDnet: a lightweight crowd density estimation model for real-time video surveillanceMuhammad Asif Khan (7367468)Hamid Menouar (16904844)Ridha Hamila (7006457)Information and computing sciencesComputer vision and multimedia computationMachine learningCrowd countingCNNDensity estimationLightweightReal-time<p dir="ltr">Automatic 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Real-Time Image Processing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11554-023-01286-8" target="_blank">https://dx.doi.org/10.1007/s11554-023-01286-8</a></p>2023-03-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11554-023-01286-8https://figshare.com/articles/journal_contribution/LCDnet_a_lightweight_crowd_density_estimation_model_for_real-time_video_surveillance/24998270CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249982702023-03-06T03:00:00Z |
| spellingShingle | LCDnet: a lightweight crowd density estimation model for real-time video surveillance Muhammad Asif Khan (7367468) Information and computing sciences Computer vision and multimedia computation Machine learning Crowd counting CNN Density estimation Lightweight Real-time |
| status_str | publishedVersion |
| title | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| title_full | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| title_fullStr | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| title_full_unstemmed | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| title_short | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| title_sort | LCDnet: a lightweight crowd density estimation model for real-time video surveillance |
| topic | Information and computing sciences Computer vision and multimedia computation Machine learning Crowd counting CNN Density estimation Lightweight Real-time |