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Feature Channel Enhancement for Crowd Counting
2020
IET Image Processing
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In this study, the authors present a featured channel enhancement (FCE) block for crowd counting. ...
A key in the design of the crowd counting system is to create a stable and accurate robust model, which requires to process on the feature channels of the counting network. ...
Inspired by squeeze-and-excitation block [4] , we propose a component named featured channel enhancement (FCE) block for the crowd counting task. ...
doi:10.1049/iet-ipr.2019.1308
fatcat:yvzbvwzub5b7dkzxfuizqxjcdq
MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting
2021
IET Image Processing
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Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. ...
The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. ...
Detection-based methods for crowd counting In the early period, the object detection methods are used for crowd counting. ...
doi:10.1049/ipr2.12175
fatcat:dzpti3i6evbazcr4hhrfgb77gq
HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting
2021
Sensors
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To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. ...
Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. ...
feature representation in the final layers is obtained by modeling the dependency among channels, thus combining the low-to-high semantic features for enhanced counting accuracy. • The proposed approach ...
doi:10.3390/s21103483
pmid:34067707
fatcat:7wcg4jd7rrcgjkhuvincpw57fy
Learning Multi-level Features to Improve Crowd Counting
2020
IEEE Access
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Early methods for crowd counting are based on manual features extraction of the human body and various regression functions [6] . ...
Third, the features processed in the first two steps are concatenated in the channel axis. The experiment shows that FFAM significantly improves the accuracy of crowd counting tasks. ...
doi:10.1109/access.2020.3039998
fatcat:tz4f6ws42fem3ldx727uutsuzm
Enhanced 3D convolutional networks for crowd counting
[article]
2019
arXiv
pre-print
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Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting. ...
Furthermore, the global contextual information is encoded into modulation weights which adaptively recalibrate channel-aware feature responses. ...
with local spatio-temporal features to boost the accuracy for crowd counting. ...
arXiv:1908.04121v1
fatcat:wpfdr3hy5fdi5gsp2lynkmaluy
Multi-scale Feature Adaptive Integration for Crowd Counting in Highly Congested Scenes
2022
IEEE Access
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In this paper, we propose a new multi-scale feature adaptive integrated network (MSFAINet) for crowd counting that adopts the multiscale feature, hybrid attention, and dilated convolution. ...
Second, it adopts a hybrid attention mechanism to enhance the receptive field of an image while reducing the loss of feature information caused by channel competition and then passes these features into ...
CONCLUSION In this work, we propose a novel multi-scale feature adaptive integration network for crowd counting. ...
doi:10.1109/access.2022.3171328
fatcat:rqh7zuekbvd35hrevojkpwy2ce
An attention mechanism-based multi-scale network crowd density estimation algorithm
2020
Zenodo
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It is becoming more and more important to calculate the people number in terms of the requirement for the safety management, because that the crowd gathering scenes are common whether or not it is daily ...
Through a large number of experiments, this network can better provide effective crowd density estimation features and improve the dissimilarity of density map distribution, which has stronger robustness ...
Large variations in crowd density between images make the counting task more challenging than other data sets, making it more difficult for crowd counting algorithms to accurately count people. ...
doi:10.5281/zenodo.4261371
fatcat:jduxsijdgva5dnwpbhb2tnuhye
HA-CCN: Hierarchical Attention-based Crowd Counting Network
2019
IEEE Transactions on Image Processing
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2019 pre-print arXiv:1907.10255v1 fatcat:n6xl6sfqibcvjdkopvk2gy4kyq
In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. ...
SAM enhances low-level features in the network by infusing spatial segmentation information, whereas the GAM focuses on enhancing channel-wise information in the higher level layers. ...
The conv3 features are enhanced by passing them through SAM. Similarly, features from conv4 and conv5 are passed through GAMs in order to perform channel-wise enhancement. ...
doi:10.1109/tip.2019.2928634
fatcat:77rugkw4b5fwxpknbq2p4r3zqi
Crowd Counting Guided by Attention Network
2020
Information
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This paper proposes a global and local attention network (GLANet) for efficient crowd counting, which applies an attention mechanism to enhance the features. ...
Crowd Crowd counting is not simply a matter of counting the numbers of people, but also requires that one obtains people's spatial distribution in a picture. ...
[22] has introduced a Bayesian model for discrete regression, which is suitable for crowd counting. ...
doi:10.3390/info11120567
fatcat:nxtgmpqbgjegtgb46bbxafkns4
MFP‐Net: Multi‐scale feature pyramid network for crowd counting
2021
IET Image Processing
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Although deep learning has been widely used for dense crowd counting, it still faces two challenges. ...
Traditional approaches for crowd counting mainly depend on regression and detection technique. ...
Such an approach preserves more fine-grained information and helps models to enhance the feature representation between channels. ...
doi:10.1049/ipr2.12230
fatcat:pzdygeszfvewpea4bwdmxcmsdy
SRNet: Scale-aware Representation Learning Network for Dense Crowd Counting
2021
IEEE Access
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Feature weighting can enhance the crowd feature, and the convolution layer provides learnable parameters for weighting. ...
MULTI-SCALE FEATURE LEARNING FOR CROWD COUNTING Scale variation is the primary problem to be solved in crowd counting; thus, the development of multi-scale feature learning methods for the density map ...
doi:10.1109/access.2021.3115963
fatcat:tgb2oyh6rvdd7mzmodrqwrjnoe
Crowd Counting via Hierarchical Scale Recalibration Network
[article]
2020
arXiv
pre-print
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The task of crowd counting is extremely challenging due to complicated difficulties, especially the huge variation in vision scale. ...
In order to reallocate channel-wise feature responses, a Scale Recalibration Module (SRM) adopts a step-by-step fusion to generate final density maps. ...
We turn to evaluate the scale invariance of the feature representations from different stages in the proposed HSRNet for diverse scenes with various crowd counts. ...
arXiv:2003.03545v1
fatcat:23vtojoeezao7gtbhxfj2xwb5i
Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah
2022
Computers Materials & Continua
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In this work, we propose a crowd management process based on detecting, tracking, and counting human faces using Artificial Intelligence techniques. ...
Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks. ...
A crowd counting and density estimation was proposed in [25] for crowd management. ...
doi:10.32604/cmc.2022.024272
fatcat:jazdy4zekzgwrbtbxpdmbqimje
SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting
[article]
2020
arXiv
pre-print
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spatial information for dense heads while first-order statistics, which can enhance the feature discrimination for the heads' areas, were used as complementary information. ...
To the best of our knowledge, we are the first work to explore the second/first-order statistics for crowd counting. ...
Introduction Crowd counting aims to count the number of people in images or videos of crowd scenes. ...
arXiv:2008.03723v1
fatcat:vcpufazxhrhjri7qpc5aiudvwy
Counting with Adaptive Auxiliary Learning
[article]
2022
arXiv
pre-print
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This paper proposes an adaptive auxiliary task learning based approach for object counting problems. ...
The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. ...
Most of the methods utilized the potential of a model itself with auxiliary tasks, such as object detection, crowd segmentation, density level classification, etc., to enhance the feature tuning for density ...
arXiv:2203.04061v1
fatcat:rii7aigvijc3hhbljlicyr2swa
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