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Perspective-Guided Convolution Networks for Crowd Counting [article]

Zhaoyi Yan, Yuchen Yuan, Wangmeng Zuo, Xiao Tan, Yezhen Wang, Shilei Wen, Errui Ding
<span title="2019-09-16">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e.  ...  Additionally, we also introduce Crowd Surveillance, a large scale dataset for crowd counting that contains 13,000+ high-resolution images with challenging scenarios.  ...  Conclusion In this paper, we present a perspective-guided convolution network (PGCNet) for crowd counting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.06966v1">arXiv:1909.06966v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sdutv52qavfjpotvqse24xb3s4">fatcat:sdutv52qavfjpotvqse24xb3s4</a> </span>
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Crowd Counting via Perspective-Guided Fractional-Dilation Convolution

Zhaoyi Yan, Ruimao Zhang, Hongzhi Zhang, Qingfu Zhang, Wangmeng Zuo
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbzicoknnzc3tjljn7ifvwpooi" style="color: black;">IEEE transactions on multimedia</a> </i> &nbsp;
To address this issue, this paper proposes a novel convolution neural network-based crowd counting method, termed Perspective-guided Fractional-Dilation Network (PFDNet).  ...  Crowd counting is critical for numerous video surveillance scenarios.  ...  CONCLUSION In this paper, we have presented a perspective-guided fractional-dilation convolutional network (PFDNet) for crowd counting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2021.3086709">doi:10.1109/tmm.2021.3086709</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fmebjsh4xrazpf6pdrrykvkshy">fatcat:fmebjsh4xrazpf6pdrrykvkshy</a> </span>
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Learning a perspective-embedded deconvolution network for crowd counting

Muming Zhao, Jian Zhang, Fatih Porikli, Chongyang Zhang, Wenjun Zhang
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pmefrmqsezb5zd3w7pf5k7fmxu" style="color: black;">2017 IEEE International Conference on Multimedia and Expo (ICME)</a> </i> &nbsp;
We present a novel deep learning framework for crowd counting by learning a perspective-embedded deconvolution network. Perspective is an inherent property of most surveillance scenes.  ...  In addition, our network allows generating density map for arbitrary-sized input in an end-to-end fashion. The proposed method achieves competitive result on the WorldExpo2010 crowd dataset.  ...  CONLCUSION In this paper we propose a perspective-embedded deconvolution network for crowd counting problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icme.2017.8019501">doi:10.1109/icme.2017.8019501</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icmcs/ZhaoZPZZ17.html">dblp:conf/icmcs/ZhaoZPZZ17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5gkvapkegrcyhjfxw2rhvluhza">fatcat:5gkvapkegrcyhjfxw2rhvluhza</a> </span>
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HAGN: Hierarchical Attention Guided Network for Crowd Counting

Zuodong Duan, Yujun Xie, Jiahao Deng
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
[13] proposed a new network structure named Perspective-Guided Convolution Networks (PGCNet), which could solve the perspective effect problem of crowd density changes in large scenes. Liu et al.  ...  Therefore, in this paper, we propose a Hierarchical Atten-tion Guided Network (HAGN) to generate high-resolution crowd density map for crowd counting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2975268">doi:10.1109/access.2020.2975268</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6weivtgdw5bktpybqzmeknpbcm">fatcat:6weivtgdw5bktpybqzmeknpbcm</a> </span>
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CrowdNet: A Deep Convolutional Network for Dense Crowd Counting [article]

Lokesh Boominathan, Srinivas S S Kruthiventi, R. Venkatesh Babu
<span title="2016-08-22">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image.  ...  Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under  ...  Our approach for crowd counting relies instead on deep learnt features using the framework of fully convolutional neural networks(CNN).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1608.06197v1">arXiv:1608.06197v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gmy3evj2cjcbhfjvw3hb4v73j4">fatcat:gmy3evj2cjcbhfjvw3hb4v73j4</a> </span>
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CrowdNet

Lokesh Boominathan, Srinivas S S Kruthiventi, R. Venkatesh Babu
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lahlxihmo5fhzpexw7rundu24u" style="color: black;">Proceedings of the 2016 ACM on Multimedia Conference - MM &#39;16</a> </i> &nbsp;
We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image.  ...  Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under  ...  Our approach for crowd counting relies instead on deep learnt features using the framework of fully convolutional neural networks(CNN).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2964284.2967300">doi:10.1145/2964284.2967300</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mm/BoominathanKB16.html">dblp:conf/mm/BoominathanKB16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3rmtdlly3jfyjiyacqf5q3xspa">fatcat:3rmtdlly3jfyjiyacqf5q3xspa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218141543/https://static.aminer.org/pdf/20170130/pdfs/mm/v0e3r1e9as5zxqjh6gfnlidgijhxwuoz.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/13/3d/133d9795a89a681c9f6db6a0244e8975992d968a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2964284.2967300"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Mask Guided GAN for Density Estimation and Crowd Counting

Hai-yan Yao, Wang-gen Wan, Xiang Li
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Adversarial learning, mask guided network, density estimation, crowd counting. 31432 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  We propose a mask guided GAN (Generative Adversarial Network) architecture to solve these two problems synthetically.  ...  FIGURE 2 . 2 The mask guided General Adversarial Network structure for density estimation and crowd counting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2973333">doi:10.1109/access.2020.2973333</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vhofxih45zda7ok6kzwpfdhfhi">fatcat:vhofxih45zda7ok6kzwpfdhfhi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108032547/https://ieeexplore.ieee.org/ielx7/6287639/8948470/08993729.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/60/c86093afb35c447f7a6ae978313f0073d14959f0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2973333"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [article]

Xiaoshuang Chen, Yiru Zhao, Yu Qin, Fei Jiang, Mingyuan Tao, Xiansheng Hua, Hongtao Lu
<span title="2021-10-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The perspective effect, which significantly influences the distribution of data points, plays an important role in crowd counting.  ...  Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e.g. persons) in images.  ...  In this section, we mainly review two mainstream improvement aspects for crowd counting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.00406v1">arXiv:2111.00406v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bwiqzljatbhblowktj5lxznrqi">fatcat:bwiqzljatbhblowktj5lxznrqi</a> </span>
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Crowd Density Estimation based on Global Reasoning

Li Wang, Fangbo Zhou, Huailin Zhao
<span title="">2020</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f5jwzvyddzf7perfd2d2qwomx4" style="color: black;">Journal of Robotics, Networking and Artificial Life (JRNAL)</a> </i> &nbsp;
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.  ...  , different scenarios and perspectives encountered in crowd counting problems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jrnal.k.201215.015">doi:10.2991/jrnal.k.201215.015</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qeyne2djo5dtras5gm6ypwwfs4">fatcat:qeyne2djo5dtras5gm6ypwwfs4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210101204138/https://www.atlantis-press.com/article/125950182.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/eb/88/eb889e661df31294baed253b84bef72d8f04ae68.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jrnal.k.201215.015"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss [article]

Qian Wang, Toby P. Breckon
<span title="2020-08-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Crowd counting is one of the keys to automatic crowd behaviour analysis. Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in recent years.  ...  Subsequently, we push the boundary of this disruptive work further by proposing a Segmentation Guided Attention Network (SGANet) with Inception-v3 as the backbone and a novel curriculum loss for crowd  ...  Index Terms-Crowd counting, Curriculum loss, Inception-v3, Segmentation guided attention networks I.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.07990v2">arXiv:1911.07990v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3rva5pim6rfsvbhnnexu26keua">fatcat:3rva5pim6rfsvbhnnexu26keua</a> </span>
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Crowd Counting Network with Self-attention Distillation

Yaoyao Li, Li Wang, Huailin Zhao, Zhen Nie
<span title="">2020</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f5jwzvyddzf7perfd2d2qwomx4" style="color: black;">Journal of Robotics, Networking and Artificial Life (JRNAL)</a> </i> &nbsp;
A B S T R A C T Context information is essential for crowd counting network to estimate crowd numbers, especially in the congested scene accurately.  ...  Then, the extracted features are processed by the dilated convolutional part for the final crowd density estimation.  ...  EXPERIMENTS Dataset This experiment uses the latest dataset UCF-QNRF [8] for crowd counting to train and test the network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jrnal.k.200528.009">doi:10.2991/jrnal.k.200528.009</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gh6hsa5snrbq7azl7wnj5usdbm">fatcat:gh6hsa5snrbq7azl7wnj5usdbm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200718062505/https://download.atlantis-press.com/article/125941065.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/aa/fc/aafcd49a8d3b364a6edc70d85dc02007699fb2ee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jrnal.k.200528.009"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting [article]

Xin Wang, Yang Zhao, Tangwen Yang, Qiuqi Ruan
<span title="2021-04-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware  ...  Extensive experiments demonstrate that the proposed approach achieves better performance than other similar state-of-the-art methods on three challenging benchmark datasets for crowd counting.  ...  [17] proposed a crowd attention convolutional network (CAT-CNN) for crowd counting, where the human in the estimated density map can get more attention by encoding a confidence map.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.02245v1">arXiv:2104.02245v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdg7wntxbfbvfe6ppolwzzehkm">fatcat:rdg7wntxbfbvfe6ppolwzzehkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210408065355/https://arxiv.org/pdf/2104.02245v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/aa/e6aa78ae8a16d88bf36ae1dd5dcf0e6efb59ce98.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.02245v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks

Muhammad Saqib, Sultan Daud Khan, Nabin Sharma, Michael Blumenstein
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Deep convolutional neural networks, crowd counting and density estimation, Motion Guided Filter, faster R-CNN.  ...  Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos.  ...  A Multi-column Convolutional Neural Network (MCNN) is proposed in [82] , which utilizes three columns with filter size of a different receptive field is used to compensate for perspective distortion.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2904712">doi:10.1109/access.2019.2904712</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sznrjdkze5bx5d5mtzb5tzksyu">fatcat:sznrjdkze5bx5d5mtzb5tzksyu</a> </span>
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An attention mechanism-based multi-scale network crowd density estimation algorithm

Huailin Zhao Yaoyao Li
<span title="2020-06-01">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
This paper proposes a multi-branch network which combines the dilated convolution and attention mechanism.  ...  By combining dilated convolution, the context information of different scales of the crowd image are extracted.  ...  For crowd counting, the attention model can be used as an effective tool to guide the network to focus on the head position, which is the most important clue of the network.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201109085018/https://zenodo.org/record/4261371/files/JICE_Vol.6_Issue1_Yaoyao%20Li_Published.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/1d/641dd514f3ec44beec281a36cd8c85c8de8901ca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4261371"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Convolutional Neural Network for Crowd Counting on Metro Platforms

Jun Zhang, Jiaze Liu, Zhizhong Wang
<span title="2021-04-17">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzoj5rayr5hutlurimhzyjlory" style="color: black;">Symmetry</a> </i> &nbsp;
In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count  ...  Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.  ...  Ltd. for the original data. Conflicts of Interest: The authors declare no conflict of interest.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210421075306/https://res.mdpi.com/d_attachment/symmetry/symmetry-13-00703/article_deploy/symmetry-13-00703.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/51/e6/51e64776e30406d32b62927042f962144787368a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym13040703"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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