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AuthorElharrouss, Omar
AuthorAlmaadeed, Noor
AuthorAl-Maadeed, Somaya
AuthorAbualsaud, Khalid
AuthorMohamed, Amr
AuthorKhattab, Tamer
Available date2023-02-23T09:13:03Z
Publication Date2022
Publication NameAVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
ResourceScopus
URIhttp://dx.doi.org/10.1109/AVSS56176.2022.9959690
URIhttp://hdl.handle.net/10576/40326
AbstractPeople counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF-QNRF, UCF-CC-50, ShangaiTech-(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.
SponsorThis research work was made possible by research grant support (QUEX-CENG-SCDL-19/20-1 ) from Supreme Committee for Delivery and Legacy (SC) in Qatar.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectComputer vision
deep reinforcement learning (DRL)
Crowd managements
Density estimation
Deep Q-network (DQN)
Learning-based segmentation
TitleCrowd counting Using DRL-based segmentation and RL-based density estimation
TypeConference Paper
Pagination-
dc.accessType Abstract Only


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