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Feature Channel Enhancement for Crowd Counting

Xingjiao Wu, Shuchen Kong, Yingbin Zheng, Hao Ye, Jing Yang, Liang He
2020 IET Image Processing  
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

Haoyu Zhao, Weidong Min, Xin Wei, Qi Wang, Qiyan Fu, Zitai Wei
2021 IET Image Processing  
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

Naveed Ilyas, Boreom Lee, Kiseon Kim
2021 Sensors  
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

Zhanqiang Huo, Bin Lu, Aizhong Mi, Fen Luo, Yingxu Qiao
2020 IEEE Access  
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]

Zhikang Zou, Huiliang Shao, Xiaoye Qu, Wei Wei, Pan Zhou
2019 arXiv   pre-print
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

Hui Gao, Miaolei Deng, Wenjun Zhao, Dexian Zhang, Yuehong Gong
2022 IEEE Access  
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

Huailin Zhao Yaoyao Li
2020 Zenodo  
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

Vishwanath A. Sindagi, Vishal M. Patel
2019 IEEE Transactions on Image Processing  
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

Pei Nie, Cien Fan, Lian Zou, Liqiong Chen, Xiaopeng Li
2020 Information  
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

Tao Lei, Dong Zhang, Risheng Wang, Shuying Li, Weijiang Zhang, Asoke K. Nandi
2021 IET Image Processing  
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

Liangjun Huang, Luning Zhu, Shihui Shen, Qing Zhang, JIANWEI Zhang
2021 IEEE Access  
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]

Zhikang Zou and Yifan Liu and Shuangjie Xu and Wei Wei and Shiping Wen and Pan Zhou
2020 arXiv   pre-print
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

Riad Alharbey, Ameen Banjar, Yahia Said, Mohamed Atri, Abdulrahman Alshdadi, Mohamed Abid
2022 Computers Materials & Continua  
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]

Haoran Duan, Shidong Wang, Yu Guan
2020 arXiv   pre-print
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]

Yanda Meng, Joshua Bridge, Meng Wei, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng
2022 arXiv   pre-print
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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