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Crowd counting via scale-adaptive convolutional neural network [article]

Lu Zhang, Miaojing Shi, Qiaobo Chen
2018 arXiv   pre-print
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.  ...  We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on  ...  Conclusion In this paper we propose a scale-adaptive convolutional neural network (SaCNN) to automatically estimate the density maps and pedestrian numbers of crowd images.  ... 
arXiv:1711.04433v4 fatcat:n4acgbv5prffhm2q5dwnyhzcdu

Crowd Counting via Scale-Adaptive Convolutional Neural Network

Lu Zhang, Miaojing Shi, Qiaobo Chen
2018 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)  
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.  ...  We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on  ...  The structure of the proposed scale-adaptive convolutional neural network (SaCNN) for crowd counting. MP: max pooling layer; DeConv: deconvolutional layer; GT: ground truth.  ... 
doi:10.1109/wacv.2018.00127 dblp:conf/wacv/ZhangSC18 fatcat:rxopahu3lbcejek3up4nyrzk3a

A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal [article]

Haoyue Bai, Jiageng Mao, S.-H. Gary Chan
2022 arXiv   pre-print
After presenting publicly available datasets and evaluation metrics, we review the recent advances with detailed comparisons on three major design modules for crowd counting: deep neural network designs  ...  This survey is to provide a comprehensive summary of recent advances on deep learning-based crowd counting techniques via density map estimation by systematically reviewing and summarizing more than 200  ...  . • Graph neural networks based method distills rich relations among multi-scale features for crowd counting.  ... 
arXiv:2012.15685v2 fatcat:kvrqnczkgbdxdnvr4243atvj3e

An Improved Multibranch Convolutional Neural Network with a Compensator for Crowd Counting

Zhiyun Zheng, Zhenhao Sun, Guanglei Zhu, Zhenfei Wang, Junfeng Wang
2022 Complexity  
Image-based crowd counting has extremely important applications in public safety issues. Most of the previous studies focused on extremely dense crowds.  ...  To solve the above problems, this paper proposes a new type of multibranch neural network with a compensator, in which features are extracted through multibranch subnetworks of different scales.  ...  attention to the application of convolutional neural networks method in crowd counting.  ... 
doi:10.1155/2022/8213855 doaj:9d811a562a114f548a215e75b9faea6d fatcat:4fefnk5f7fhv3lfckjqyshupxy

An Automatic Scale-adaptive Approach with Attention Mechanism-based Crowd Spatial Information for Crowd Counting

Weihang Kong, He Li, Guanglong Xing, Fengda Zhao
2019 IEEE Access  
This paper proposes an automatic scale-adaptive approach with attention mechanism-based crowd spatial information addressing the crowd counting task, i.e. a novel cascaded crowd counting network.  ...  INDEX TERMS Crowd counting, scale-adaptive mechanism, attention mechanism, spatial information, feature fusion.  ...  Since the recent models based on convolutional neural network have achieved significant improvement on crowd counting.  ... 
doi:10.1109/access.2019.2918936 fatcat:mcrvyp6opvavvkaxyirh3pgqsu

Crowd Counting in Images via DSMCNN

Yu-qian ZHANG, Wen-qian WANG, Tao WANG, Guo-hui LI, Jun LEI
2019 DEStech Transactions on Computer Science and Engineering  
In this work, we propose a Dilated Stacked Multi-column Convolutional Neural Network architecture for crowd density estimation in still single images.  ...  Counting the crowd from a single image accurately is always a challenging task.  ...  In this paper, we propose a novel Convolutional Neural Network for crowd density estimation and crowd counting aimed at address these problems.  ... 
doi:10.12783/dtcse/iteee2019/28743 fatcat:q2zkbfv5ync3fnx2aaq7p4hjbe

Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks

Liping Zhu, Hong Zhang, Sikandar Ali, Baoli Yang, Chengyang Li
2020 Journal of Intelligent Systems  
Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields.  ...  In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd.  ...  To solve these issues based on the multi-column CNN [19] which has a success of working in the crowd counting, a new crowd counting framework called Multi-Scale Adversarial Convolutional Neural Network  ... 
doi:10.1515/jisys-2019-0157 fatcat:bhj42iinorekdcz2jruryus5uq

Crowd Density Estimation based on Global Reasoning

Li Wang, Fangbo Zhou, Huailin Zhao
2020 Journal of Robotics, Networking and Artificial Life (JRNAL)  
The crowd counting task has made massive progress by now due to the Convolutional Neural Network (CNN).  ...  In this paper, we propose a Graph-based Global Reasoning (GGR) network for crowd counting to solve this problem.  ...  Ma, Single-image crowd counting via multi-column convolutional neural network Table 1 | 1 The experimental results on the UCF-QNRF dataset Method MAE MSE Idrees 2013 [13] 315 508 MCNN [1] 277  ... 
doi:10.2991/jrnal.k.201215.015 fatcat:qeyne2djo5dtras5gm6ypwwfs4

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
for Anomaly Detection Wang, Yongfang Enhanced Saliency Prediction via Orientation Selectivity Wang, Yuchen Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for Image  ...  Improving Robustness of DNNs against Comm Corruptions via Gaussian Adversarial Training Wan, Zekang Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for Image Denoising  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, Yi Ma
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.  ...  This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective.  ...  Multi-column CNN for Crowd Counting Density map based crowd counting To estimate the number of people in a given image via the Convolutional Neural Networks (CNNs), there are two natural configurations  ... 
doi:10.1109/cvpr.2016.70 dblp:conf/cvpr/ZhangZCGM16 fatcat:gyhtabgdzfdbrm4yx6t2ji4ihm

People, Penguins and Petri Dishes: Adapting Object Counting Models to New Visual Domains and Object Types Without Forgetting

Mark Marsden, Kevin McGuinness, Suzanne Little, Ciara E. Keogh, Noel E. O'Connor
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function  ...  The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife.  ...  Heatmap-based crowd counting using a fully convolutional neural network was firstly investigated by Zhang et al. [13] and subsequently by Marsden et al.  ... 
doi:10.1109/cvpr.2018.00842 dblp:conf/cvpr/MarsdenMLKO18 fatcat:mc5rystcqzazlhm52opxqdwlky

An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting [article]

Diptodip Deb, Jonathan Ventura
2018 arXiv   pre-print
We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting.  ...  Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd).  ...  The work of [27] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.  ... 
arXiv:1804.07821v1 fatcat:tc2bvvcyevhgjj3sen3zgtc4i4

An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

Diptodip Deb, Jonathan Ventura
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting.  ...  Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd).  ...  The work of [27] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.  ... 
doi:10.1109/cvprw.2018.00057 dblp:conf/cvpr/DebV18 fatcat:6mkwmbq5gbhzdmn56hmqjvb2km

People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting [article]

Mark Marsden, Kevin McGuinness, Suzanne Little, Ciara E. Keogh, Noel E. O'Connor
2017 arXiv   pre-print
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function  ...  The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife.  ...  Heatmap-based crowd counting using a fully convolutional neural network was firstly investigated by Zhang et al. [13] and subsequently by Marsden et al.  ... 
arXiv:1711.05586v1 fatcat:yhblgewd5bgo3ohh4xyvhif3mm

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Naveed Ilyas, Ahsan Shahzad, Kiseon Kim
2019 Sensors  
We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques.  ...  Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd  ...  The authors in [26] focused on conventional and convolutional-neural-network (CNN)-based single-image crowd-counting techniques.  ... 
doi:10.3390/s20010043 pmid:31861734 pmcid:PMC6983207 fatcat:gvso42grpjbw5ptdb23sdfeuwu
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