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Crowd Counting using Deep Recurrent Spatial-Aware Network

Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
region from the crowd density map and transforms it to the suitable scale and rotation for optimal crowd estimation; ii) a Local Refinement Network that refines the density map of the attended region  ...  In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise  ...  To address the aforementioned concerns, we propose a Deep Recurrent Spatial-Aware Network for crowd counting.  ... 
doi:10.24963/ijcai.2018/118 dblp:conf/ijcai/LiuWLOL18 fatcat:7kycqlqlsveebmxexqb5xyp6fm

Crowd Counting using Deep Recurrent Spatial-Aware Network [article]

Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin
2018 arXiv   pre-print
region from the crowd density map and transforms it to the suitable scale and rotation for optimal crowd estimation; ii) a Local Refinement Network that refines the density map of the attended region  ...  In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise  ...  To address the aforementioned concerns, we propose a Deep Recurrent Spatial-Aware Network for crowd counting.  ... 
arXiv:1807.00601v1 fatcat:lupdprt3engs3k2pk6wp6scdcu

Learning Spatial Awareness to Improve Crowd Counting [article]

Zhi-Qi Cheng, Jun-Xiu Li, Qi Dai, Xiao Wu, Alexander Hauptmann
2019 arXiv   pre-print
In this paper, we present a novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting.  ...  The proposed framework can be integrated into existing deep crowd counting methods and is end-to-end trainable.  ...  The main contribution of this work is the proposed Spatial Awareness Network and Maximum Excess over Pixels loss for addressing the issue of crowd counting.  ... 
arXiv:1909.07057v1 fatcat:ssram3xwrjed3oz44bomdkla6m

SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

Liyan Xiong, Hu Yi, Xiaohui Huang, Weichun Huang, Nouman Ali
2022 Mathematical Problems in Engineering  
To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality  ...  SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then  ...  dense crowd counting research. erefore, in this section, we introduce some related research on mainstream CNN-based crowd counting algorithms and attention modules.  ... 
doi:10.1155/2022/3277995 fatcat:42tnlg5hebgpjh4k3mfqh7l3cq

Relevant Region Prediction for Crowd Counting [article]

Xinya Chen, Yanrui Bin, Changxin Gao, Nong Sang, Hao Tang
2020 arXiv   pre-print
In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM).  ...  Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency.  ...  Related Work In this section, we will introduce the related work on crowd counting and graph convolutional network.  ... 
arXiv:2005.09816v1 fatcat:pninkkstc5hatcqsdlwek4uqlm

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Yani Zhang, Huailin Zhao, Zuodong Duan, Liangjun Huang, Jiahao Deng, Qing Zhang
2021 Sensors  
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).  ...  Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which  ...  Section 2 reviews related work regarding crowd counting and crowd localization. Section 3 presents the proposed method for crowd counting and localization.  ... 
doi:10.3390/s21113777 pmid:34072408 fatcat:sben5kwjqnbsbmdiqvg3lcbl5y

CASA-Crowd: A Context-aware Scale Aggregation CNN-based Crowd Counting Technique

Naveed Ilyas, Ashfaq Ahmad, Kiseon Kim
2019 IEEE Access  
Crowd Counting method (CASA-Crowd) to obtain the deep, varying scale and perspective varying features.  ...  To overcome these drawbacks and to provide a state-of-the-art counting accuracy with comparable computational cost, we therefore propose a deeper and wider network: a Context-aware Scale Aggregation CNN-based  ...  DENSITY MAP ESTIMATION 1) CASA-CROWD:A CONTEXT-AWARE SCALE AGGREGATION CNN-BASED CROWD COUNTING TECHNIQUE A Context-aware Scale Aggregation CNN-based Crowd Counting method (CASA-Crowd) is depicted  ... 
doi:10.1109/access.2019.2960292 fatcat:pima4au4fnavldy6aqsrzfrrza

A survey of recent advances in CNN-based single image crowd counting and density estimation

Vishwanath A. Sindagi, Vishal M. Patel
2018 Pattern Recognition Letters  
In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey.  ...  The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective.  ...  The network is evaluated for cross-scene crowd counting as well as single scene crowd counting and superior results are demonstrated for both scenarios.  ... 
doi:10.1016/j.patrec.2017.07.007 fatcat:ex3rtmm2jberzjcyebjpaaeluy

Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting [article]

Pongpisit Thanasutives, Ken-ichi Fukui, Masayuki Numao, Boonserm Kijsirikul
2020 arXiv   pre-print
In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting.  ...  The combination yields an effective model for counting in both dense and sparse crowd scenes.  ...  [19] applied an attention mechanism to crowd counting by integrating an attention-aware network into a multi-scale deformable network to detect crowd regions. Wang et al.  ... 
arXiv:2003.05586v5 fatcat:ivhikuwchzejhirfjw7iidmcka

PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting [article]

Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Lei Liu
2020 arXiv   pre-print
In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware  ...  Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast scale variations in crowd density within the interested area, and severe occlusion among the  ...  CONCLUSION In this work, we introduced a novel deep architecture called Pyramid Density-Aware Attention-based network (PDANet) for crowd counting.  ... 
arXiv:2001.05643v10 fatcat:h6wiqwaeqja4ffsgdm3kikgw34

FSC-Set: Counting, Localization of Football Supporters Crowd in the Stadiums

Omar Elharrouss, Noor Almaadeed, Khalid Abualsaud, Somaya Al-Maadeed, Ali Al-Ali, Amr Mohamed
2022 IEEE Access  
Further, we propose a CNN-based method named FSCNet for crowd counting exploiting context-aware attention, spatial-wise attention, and channel-wise attention modules.  ...  This paper proposes two main contributions including a new dataset for crowd counting, and a CNN-based method for counting the number of people and generating the crowd density maps.  ...  Also for handling the scale variations for crowd counting, the authors in [33] proposed a multi-scale convolutional module and self-attention residual network that are fused for generating the crowd  ... 
doi:10.1109/access.2022.3144607 fatcat:udaswzcxjfgbbjlkvxvjkqgbpa

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

Naveed Ilyas, Ahsan Shahzad, Kiseon Kim
2019 Sensors  
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  ...  Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety  ...  [81] proposed a deep recurrent spatially aware network in which a spatial-transformer module was used for counting while simultaneously tackling both scale and rotation variations. Remark 1.  ... 
doi:10.3390/s20010043 pmid:31861734 pmcid:PMC6983207 fatcat:gvso42grpjbw5ptdb23sdfeuwu

Multi-Scale Attention Network for Crowd Counting [article]

Rahul Rama Varior, Bing Shuai, Joseph Tighe, Davide Modolo
2019 arXiv   pre-print
To address this issue, we propose a novel multi-branch scale-aware attention network that exploits the hierarchical structure of convolutional neural networks and generates, in a single forward pass, multi-scale  ...  In crowd counting datasets, people appear at different scales, depending on their distance from the camera.  ...  Related work Multi-scale models for crowd counting. Crowd counting datasets contain very large variation of people sizes, due to large perspective changes.  ... 
arXiv:1901.06026v3 fatcat:m3yzjvalszdlhalpewpcergxem

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.  ...  To address these issues, a multiscale feature pyramid network (MFP-Net) for dense crowd counting is proposed in this paper. The proposed MFP-Net makes two contributions.  ...  METHOD In this paper, we propose a multi-scale feature pyramid network (MFP-Net) and apply it to the field of crowd counting and density map estimation.  ... 
doi:10.1049/ipr2.12230 fatcat:pzdygeszfvewpea4bwdmxcmsdy

Deep Learning for Crowd Counting: A Survey

Tjeng Wawan Cenggoro
2019 Engineering, Mathematics and Computer Science Journal (EMACS)  
This paper aims to capture a big picture of existing deep learning models for crowd counting. Hence, the development of novel models for future works can be accelerated.  ...  The growth of deep learning for crowd counting is immense in the recent years. This results in numerous deep learning model developed with huge multifariousness.  ...  In the following year after MSCNN introduction, there are three works that used single-column scale-aware CNN for crowd counting: Single Column Networks (SCNet), Scale Aggregation Networks (SANet), and  ... 
doi:10.21512/emacsjournal.v1i1.5794 fatcat:ryrwisbarnc6fj37v4wfpgynbe
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