Results 1 to 10 of about 124,762 (273)
Congested Crowd Counting via Adaptive Multi-Scale Context Learning [PDF]
Sensors, 2021 In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet).
Yani Zhang +5 more
doaj +2 more sources
Crowd counting: a behavioural economics perspective. [PDF]
Qual Quant, 2021 Developments in technology have facilitated the emergence of new crowd counting organisations. Some of the organisations have established platforms to disseminate their data, making it available to researchers for the first time. These databases promise to increase the quality and quantity of research in various fields.
Phillips PJ, Pohl G.
europepmc +5 more sources
Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting [PDF]
Sensors, 2022 In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation.
Liangjun Huang +4 more
doaj +2 more sources
A Crowd Counting Framework Combining with Crowd Location [PDF]
Journal of Advanced Transportation, 2021 In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics.
Jin Zhang +5 more
doaj +2 more sources
Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection [PDF]
Journal of Imaging, 2020 Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a ...
Sorn Sooksatra +3 more
doaj +2 more sources
Crowd Counting with Decomposed Uncertainty
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed.
Oh, Min-hwan +2 more
core +3 more sources
HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting [PDF]
Sensors, 2021 Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments.
Naveed Ilyas, Boreom Lee, Kiseon Kim
doaj +2 more sources
Crowd counting in domain generalization based on multi-scale attention and hierarchy level enhancement [PDF]
Scientific ReportsIn order to solve the problem of weak single domain generalization ability in existing crowd counting methods, this study proposes a new crowd counting framework called Multi-scale Attention and Hierarchy level Enhancement (MAHE).
Jiarui Zhou, Jianming Zhang, Yan Gui
doaj +2 more sources
Encoder-Decoder Network Fusing Channel and Spatial Attention for Crowd Counting [PDF]
Jisuanji kexue yu tansuo, 2022 The purpose of crowd counting is to accurately predict the number, distribution and density of crowds in real scenes. However, crowd counting often suffers from some problems such as complex background, diverse target scales, and cluttered crowd ...
YU Ying, PAN Cheng, ZHU Huilin, QIAN Jin, TANG Hong
doaj +1 more source
Fine-Grained Crowd Counting [PDF]
IEEE Transactions on Image Processing, 2021 Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many practical applications, the total number of people in an image is not as useful as the number of people in each sub-category. E.g., knowing the number of people waiting inline or browsing can
Jia Wan +2 more
openaire +3 more sources