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المؤلفElharrouss, Omar
المؤلفMohammed, Hanadi Hassen
المؤلفAl-Maadeed, Somaya
المؤلفAbualsaud, Khalid
المؤلفMohamed, Amr
المؤلفKhattab, Tamer
تاريخ الإتاحة2024-10-10T11:16:39Z
تاريخ النشر2024-01-01
اسم المنشور2024 International Conference on Intelligent Systems and Computer Vision, ISCV 2024
المعرّفhttp://dx.doi.org/10.1109/ISCV60512.2024.10620151
الاقتباسElharrouss, O., Mohammed, H. H., Al-Maadeed, S., Abualsaud, K., Mohamed, A., & Khattab, T. (2024, May). Crowd density estimation with a block-based density map generation. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.‏
الترقيم الدولي الموحد للكتاب [9798350350180]
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202351369&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/60023
الملخصCrowd management is one of the challenging tasks in computer vision especially crowd counting which can be the key solution for many surveillance applications. But the estimation of crowdedness in a scene can be related to many problems that limit the effectiveness of any method, we can cote from the theme the scale variation of the objects, and the similarity between the background and the foreground in some complex scenes, as well as the variation of the degree of crowdecity within the same analyzed data. In this paper, we propose a block-based crowd counting model by collaborating the VGG layer with channel-wise attention modules between each block of layers (Crowd-per-Block). the channel attention is used to distinguish between the background and foreground texture. At the end of the network and to extract the contextual information and capture the change in density distribution we introduced a cascaded-spatial-wise attention module. The proposed method is evaluated on various datasets. The experimental results show that the proposed method works well for fully crowded scenes while it's less accurate for less crowded scenes.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعcascaded-spatial-wise attention
channel-wise attention
CNN
Crowd counting
density estimation map
العنوانCrowd density estimation with a block-based density map generation
النوعConference
dc.accessType Open Access


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