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Counting congested crowds under wild conditions with a multi-task Inception network

Biao Yang, Jinmeng Cao, Nan Wang, Yuyu Zhang, Ling Zou
2017 Communications in Information and Systems  
Counting performance obtained through multi-task learning is superior to that obtained through only estimating density map.  ...  The network can jointly estimate the density map, crowd density level, and background / foreground separation.  ...  Aside from counting approaches designed for addressing scale variation, cascaded-MTL predicts the crowd counts in a multi-task manner, which jointly estimates the density map and the crowd density level  ... 
doi:10.4310/cis.2017.v17.n1.a1 fatcat:cn4q3awztvcwbdj3uj3wex6d7a

CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting [article]

Vishwanath A. Sindagi, Vishal M. Patel
2017 arXiv   pre-print
In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation.  ...  Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network.  ...  Conclusions In this paper, we presented a multi-task cascaded CNN network for jointly learning crowd count classification and density map estimation.  ... 
arXiv:1707.09605v2 fatcat:fijzgbi6pvegbd5git5l6hf7zu

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
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  ...  With the recent development of deep learning techniques, crowd counting has aroused much attention and achieved great success in recent years.  ...  The original annotation process for crowd counting via density map estimation is point-level annotation, which is labor-intensive, HA-CCN [159] proposed a weakly supervised learning setup and leveraged  ... 
arXiv:2012.15685v2 fatcat:kvrqnczkgbdxdnvr4243atvj3e

CNN-based Density Estimation and Crowd Counting: A Survey [article]

Guangshuai Gao, Junyu Gao, Qingjie Liu, Qi Wang, Yunhong Wang
2020 arXiv   pre-print
In this paper, we have surveyed over 220 works to comprehensively and systematically study the crowd counting models, mainly CNN-based density map estimation methods.  ...  Meanwhile, density map generation and evaluation tools are also provided.  ...  ACKNOWLEDGMENT The authors would like to thank reviewers for their valuable suggestions and comments.  ... 
arXiv:2003.12783v1 fatcat:uqsoismxkzft7audwvdpr3dt7q

Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives [article]

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
2022 arXiv   pre-print
Crowd counting is an effective tool for situational awareness in public places.  ...  We also sort the well-known crowd counting models by their performance over benchmark datasets.  ...  Supervised and Weakly-supervised Learning The state-of-the-art methods for crowd density estimation use point-level supervision (fully supervised).  ... 
arXiv:2209.07271v1 fatcat:nlu4ezjcujblroy26vjhnj2p7m

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  
Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning.  ...  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.  ...  a mapping from low-level feature to the crowd count.  ... 
doi:10.1016/j.patrec.2017.07.007 fatcat:ex3rtmm2jberzjcyebjpaaeluy

Multi‐level features extraction network with gating mechanism for crowd counting

Xin Zeng, Qiang Guo, Haoran Duan, Yunpeng Wu
2021 IET Image Processing  
Unlike previous works, a multi-level features extraction network with gating mechanism for crowd counting is proposed.  ...  The multi-level features extraction network learns to fuse features from multiple levels and reduce false predictions.  ...  [19] presented a new model, named MMNet, for crowd counting by learning multi-level spatial information.  ... 
doi:10.1049/ipr2.12304 fatcat:gxx5eesitrasfeuympkqrrmwfq

AAFM: Adaptive Attention Fusion Mechanism for Crowd Counting

Zuodong Duan, Huimin Chen, Jiahao Deng
2020 IEEE Access  
The AAFM is able to generate high-quality crowd density maps for accurate targets counting.  ...  It is able to obtain both low-level detail information and highlevel semantic information for generating high-quality crowd density maps. III.  ... 
doi:10.1109/access.2020.3012818 fatcat:rx2qkgs6svb4beqrkjs7rhpz2u

Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting [article]

Chenfeng Xu, Kai Qiu, Jianlong Fu, Song Bai, Yongchao Xu, Xiang Bai
2019 arXiv   pre-print
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels.  ...  Existing approaches mainly suffer from the extreme density variances. Such density pattern shift poses challenges even for multi-scale model ensembling.  ...  Cnn-based cascaded multi- for localised crowd counting.  ... 
arXiv:1907.12428v2 fatcat:b5yi7hzzpreozndyqjgn7qyzpe

Cascaded Residual Density Network for Crowd Counting [article]

Kun Zhao, Luchuan Song, Bin Liu, Qi Chu, Nenghai Yu
2021 arXiv   pre-print
In this paper, we propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately. (1) We estimate the  ...  residual density maps by multi-scale pyramidal features through cascaded residual density modules.  ...  is supported by the National Natural Science Foundation of China (Grant No. 61371192), the Key Laboratory Foundation of the Chinese Academy of Sciences (CXJJ-17S044) and the Fundamental Research Funds for  ... 
arXiv:2107.13718v1 fatcat:rkcpepywjvcozphnydmx5a2f2a

Crowd Counting Using Scale-Aware Attention Networks [article]

Mohammad Asiful Hossain, Mehrdad Hosseinzadeh, Omit Chanda, Yang Wang
2019 arXiv   pre-print
Given the estimated density map, the final crowd count can be obtained by summing over all values in the density map. One challenge of crowd counting is the scale variation in images.  ...  Given an image of a crowded scene, our goal is to estimate the density map of this image, where each pixel value in the density map corresponds to the crowd density at the corresponding location in the  ...  We thank NVIDIA for donating some of the GPUs used in this work. Figure 6 . Qualitative examples of density maps.  ... 
arXiv:1903.02025v1 fatcat:s6ivmtoozngahny3gwiryexaea

Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting [article]

Liang Zhu, Zhijian Zhao, Chao Lu, Yining Lin, Yao Peng, Tangren Yao
2019 arXiv   pre-print
maps for highly congested crowd scenes.  ...  These dual path multi-scale fusion networks have the same structure, one path is responsible for generating attention map by highlighting crowd regions in images, the other path is responsible for fusing  ...  [28] propose a deep convolutional neural network for crowd counting with two related learning objectives, crowd density and crowd count.  ... 
arXiv:1902.01115v1 fatcat:nhoupss6ungbpe6taxt3dpepja

Crowd counting with crowd attention convolutional neural network

Jiwei Chen, Su Wen, Zengfu Wang
2019 Neurocomputing  
The crowd count can be obtained by integrating the final density map.  ...  To encode a highly refined density map, the total crowd count of each image is classified in a designed classification task and we first explicitly map the prior of the population-level category to feature  ...  Please cite @article{CHEN2020, title={Crowd counting with crowd attention convolutional neural network}, au-thor={ Chen, Jiwei and Wen, Su and Wang, Zengfu}, journal={Neurocomputing}, volume={382}, pages  ... 
doi:10.1016/j.neucom.2019.11.064 fatcat:adhjwzxt35hn3mroxq4cdxm7sy

Deep Learning for Crowd Counting: A Survey

Tjeng Wawan Cenggoro
2019 Engineering, Mathematics and Computer Science Journal (EMACS)  
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.  ...  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.  ...  Meanwhile, ResNetCrowd is subjected to multi-task learning of four tasks: behavior recognition, density level classification, count regression, and density map regression.  ... 
doi:10.21512/emacsjournal.v1i1.5794 fatcat:ryrwisbarnc6fj37v4wfpgynbe

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

Naveed Ilyas, Ashfaq Ahmad, Kiseon Kim
2019 IEEE Access  
To handle these challenges most of the crowd counting methods use multi-columns (restrict themselves to a set of specific density scenes), deploying a deeper and multi-networks for density estimation.  ...  INDEX TERMS Deep learning, convolutional neural networks, density estimation, crowd counting. 182050 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  For example, a pre-classification network classifies the patches of different density level and rest of the CNN-based crowd counting model is trained on specific range of density levels.  ... 
doi:10.1109/access.2019.2960292 fatcat:pima4au4fnavldy6aqsrzfrrza
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