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Cascaded Multi-task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting

Desen Zhou, Qian He
2020 IEEE Access  
The multi-task strategy allows the network to explicitly attent to foreground regions of a crowd scene and improve density regression.  ...  We develop a cascaded depth-aware counting network that jointly performs head segmentation and density map regression.  ...  Inspired by the pose estimation methods [26] , [27] , we propose a cascaded network for RGBD crowd counting.  ... 
doi:10.1109/access.2020.2998678 fatcat:kioq2adsbfcwddhrydcxdnvnha

Multi-Scale Guided Attention Network for Crowd Counting

Pengfei Li, Min Zhang, Jian Wan, Ming Jiang
2021 Scientific Programming  
In this paper, we propose a multi-scale guided attention network (MGANet) to solve the above problems.  ...  The attention mechanism is used to guide the acquired features of each layer in space and channel so that the network pays more attention to the crowd in the image, ignores irrelevant information, and  ...  Figure 2 : 2 Figure 2: e architecture of a multi-scale guided attention network.  ... 
doi:10.1155/2021/5596488 doaj:f35db0db71fa4670ad5332372dbe6136 fatcat:xr2hfutv75ehbenszebvivjafq

HAGN: Hierarchical Attention Guided Network for Crowd Counting

Zuodong Duan, Yujun Xie, Jiahao Deng
2020 IEEE Access  
[3] proposed a new kind of learning strategy named Multi-column Convolutional Neural Network (McML) for multi-column networks, which could effectively solve the multi-scale learning problem of the network  ...  Therefore, in this paper, we propose a Hierarchical Atten-tion Guided Network (HAGN) to generate high-resolution crowd density map for crowd counting.  ... 
doi:10.1109/access.2020.2975268 fatcat:6weivtgdw5bktpybqzmeknpbcm

Hybrid attention network based on progressive embedding scale-context for crowd counting [article]

Fusen Wang and Jun Sang and Zhongyuan Wu and Qi Liu and Nong Sang
2021 arXiv   pre-print
The existing crowd counting methods usually adopted attention mechanism to tackle background noise, or applied multi-level features or multi-scales context fusion to tackle scale variation.  ...  Besides, we embed certain scale-context to the hybrid attention along the spatial and channel dimensions for alleviating these counting errors caused by the variation of perspective and head scale.  ...  It also works for crowd counting.  ... 
arXiv:2106.02324v1 fatcat:ykhlnlgsl5bszmxxnpp33srdbq

Attention to Head Locations for Crowd Counting [article]

Youmei Zhang, Chunluan Zhou, Faliang Chang, Alex C. Kot
2018 arXiv   pre-print
The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolution neural network for crowd density estimation, which  ...  In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting.  ...  For crowd counting, the attention model could be an effective tool to guide the network focusing on head locations, which are the most important cue for crowd counting.  ... 
arXiv:1806.10287v1 fatcat:o3e6lxgkjjayhd377hbia2nasm

PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images [article]

Guangshuai Gao, Qingjie Liu, Zhenghui Hu, Lu Li, Qi Wen, Yunhong Wang
2022 arXiv   pre-print
), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM.  ...  Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.  ...  ASPDNet [7] , [8] integrates a scale pyramid module to capture multi-scale information for counting in remote sensing images. D.  ... 
arXiv:2012.03597v3 fatcat:m5adcmcfgbcgpgq47wrwwvsapq

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.  ...  Multi-tasking CNN 3. CNN with local context 4. CNN with ensemble learning 5. Generative Adversarial Networks (GAN) for crowd counting 6.  ... 
doi:10.21512/emacsjournal.v1i1.5794 fatcat:ryrwisbarnc6fj37v4wfpgynbe

NAS-Count: Counting-by-Density with Neural Architecture Search [article]

Yutao Hu, Xiaolong Jiang, Xuhui Liu, Baochang Zhang, Jungong Han, Xianbin Cao, David Doermann
2020 arXiv   pre-print
Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense  ...  In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet).  ...  Through this macro-level search, we extend PC-DARTS from the single-path search strategy to a newly multi-path search strategy, which is more suitable for discovering a multi-scale network for crowd counting  ... 
arXiv:2003.00217v2 fatcat:a7jwdofkxrhfzjsdezdmelhdni

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.  ...  Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes.  ...  Fig. 1 provides an overview of the proposed attention-based feature concatenation for multi-scale crowd counting.  ... 
doi:10.1109/tip.2019.2928634 fatcat:77rugkw4b5fwxpknbq2p4r3zqi

Behaviour detection in crowded classroom scenes via enhancing features robust to scale and perspective variations

Mingyu Liu, Fanman Meng, Qingbo Wu, Linfeng Xu, Qianghua Liao
2021 IET Image Processing  
First, an attention-based RoI (regionof-interest) extractor is designed to handle scale variation.  ...  Detecting human behaviours in images of crowded classroom scenes is a challenging task, due to the large variations of humans in scale and pose perspective.  ...  Grid R-CNN [22] proposes a grid point guided localization mechanism to exploit the position sensitive property of fully convolutional networks for high quality localization.  ... 
doi:10.1049/ipr2.12318 fatcat:io7od4l46bb2vhmhoajgfxbogi

Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

Rafik Gouiaa, Moulay A. Akhloufi, Mozhdeh Shahbazi
2021 Big Data and Cognitive Computing  
The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets.  ...  Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective.  ...  [52] used the attention mechanism to introduce a scale-aware attention network to address the scale variation in crowd counting images.  ... 
doi:10.3390/bdcc5040050 fatcat:tlmoxgtixzeqlhqkdundbwq6rq

JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method [article]

Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
2020 arXiv   pre-print
Considering this, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations.  ...  Furthermore, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation.  ...  Sreevali for providing assistance in annotation and verification efforts.  ... 
arXiv:2004.03597v2 fatcat:jztuu4m76vdznpjimneqk3v4sm

Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting

Yang Yu, Jifeng Huang, Wen Du, Naixue Xiong
2019 Sensors  
This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints.  ...  For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size.  ...  Acknowledgments: The authors are grateful for the free copyright sharing of the datasets mentioned in this paper, as well as previous awesome inspiring works on crowd counting.  ... 
doi:10.3390/s19092013 fatcat:5zlov5tmk5co5g3g3gjwm5uqo4

Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting [article]

Vishwanath A Sindagi, Vishal M. Patel
2019 arXiv   pre-print
This is usually due to their limited abilities in effectively combining the multi-scale features for problems like crowd counting.  ...  Furthermore, in order to increase the effectiveness of the multi-scale fusion, we employ a principled way of generating scale-aware ground-truth density maps for training.  ...  counting by introducing a learning to rank framework [34] , cascaded feature fusion [43] and scale-based feature aggregation [7] , weakly-supervised learning for crowd counting [58] .  ... 
arXiv:1908.10937v1 fatcat:xcxnok36w5bcjg25irvwt6kkxy

Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method [article]

Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
2019 arXiv   pre-print
Furthermore, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD) that is ~2.8 larger than the most recent crowd counting datasets in terms of the number of images.  ...  In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation.  ...  [12] 113.0 188.0 CG-DRCN (proposed) 112.2 176.3 column network (MCNN) [61] , cascaded multi-task learning for crowd counting (CMTL) [43] , Switching-CNN [38] , CSR-Net [20] and SANet [4] 2 .  ... 
arXiv:1910.12384v1 fatcat:zxvhjgbanzh3lbfk4uba5nwklu
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