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1309
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learning (artificial intelligence), neural nets,
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Crowd counting, Multi-column CNN, Multi-task, Per-scale loss, Density map
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Crowd counting, Density estimation, Crowd analysis
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Sheng, B.,
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Crowd Counting via Weighted VLAD on a Dense Attribute Feature Map,
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1808
Semantics, Feature extraction, Image representation, Encoding, Roads,
Neural networks, Image segmentation, Crowd counting,
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1810
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Correlation Net, CNN, Activity recognition, Deep learning, Fusion
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Wang, Q.,
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1811
Feature extraction, Training, Machine learning,
Distance measurement, Learning systems,
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Ma, T.J.[Tian-Jun],
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1902
Deep learning, Boosting learning, Attribute learning,
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1903
Estimation, Feature extraction, Scalability, Reliability, Cameras,
Head, Support vector machines, Pedestrian counting,
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ITS(20), No. 5, May 2019, pp. 1728-1738.
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1905
Training, Task analysis, Image sequences, Redundancy,
Intelligent transportation systems, Feature extraction, Labeling,
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Beyond Counting: Comparisons of Density Maps for Crowd Analysis
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1905
Feature extraction, Task analysis, Forestry, Estimation,
Image resolution, Videos, Measurement,
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Ling, M.,
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Indoor Crowd Counting by Mixture of Gaussians Label Distribution
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IP(28), No. 11, November 2019, pp. 5691-5701.
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1909
Videos, Head, Adaptation models, Feature extraction, Cameras,
Estimation, Gaussian distribution, Label ambiguity,
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Miao, Y.Q.[Yun-Qi],
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1909
Crowd counting, Spatio-temporal feature, Crowd analysis
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Xu, M.[Mingliang],
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1909
Crowd counting, Depth information, Pedestrian detection, Density estimation
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Shami, M.B.,
Maqbool, S.,
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Ayaz, Y.,
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People Counting in Dense Crowd Images Using Sparse Head Detections,
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IEEE DOI
1909
Head, Feature extraction, Training, Detectors,
Support vector machines, Training data, Estimation,
head detection
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Sindagi, V.A.,
Patel, V.M.,
HA-CCN: Hierarchical Attention-Based Crowd Counting Network,
IP(29), No. , 2020, pp. 323-335.
IEEE DOI
1910
convolutional neural nets, feature extraction, image annotation,
image segmentation, learning (artificial intelligence),
crowd analytics
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Li, H.[He],
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IET-CV(13), No. 6, September 2019, pp. 556-561.
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Tian, Y.,
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PaDNet: Pan-Density Crowd Counting,
IP(29), 2020, pp. 2714-2727.
IEEE DOI
2001
Crowd counting, density level analysis, pan-density evaluation,
convolutional neural networks
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Zhao, M.M.[Mu-Ming],
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Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting,
CVPR19(12728-12737).
IEEE DOI
2002
BibRef
Zhang, W.[Wei],
Wang, Y.J.[Yong-Jie],
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IET-IPR(14), No. 4, 27 March 2020, pp. 621-627.
DOI Link
2003
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Wu, Q.[Qin],
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Single-image crowd counting: a comparative survey on deep
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MultInfoRetr(9), No. 2, June 2020, pp. 63-80.
Springer DOI
2005
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Zhu, M.[Ming],
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PRL(135), 2020, pp. 279-285.
Elsevier DOI
2006
Crowd counting, Density estimation,
Convolutional neural network, Soft attention mechanism
BibRef
Liu, Y.,
Wen, Q.,
Chen, H.,
Liu, W.,
Qin, J.,
Han, G.,
He, S.,
Crowd Counting Via Cross-Stage Refinement Networks,
IP(29), 2020, pp. 6800-6812.
IEEE DOI
2007
Feature extraction, Convolution, Decoding, Clutter,
Benchmark testing, Cameras, Network architecture, Crowd counting,
image refinement
BibRef
Mo, H.,
Ren, W.,
Xiong, Y.,
Pan, X.,
Zhou, Z.,
Cao, X.,
Wu, W.,
Background Noise Filtering and Distribution Dividing for Crowd
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IP(29), 2020, pp. 8199-8212.
IEEE DOI
2008
Head, Noise measurement, Estimation, Robustness, Feature extraction,
Crowd counting, head size estimation,
head mask
BibRef
Jiang, S.,
Lu, X.,
Lei, Y.,
Liu, L.,
Mask-Aware Networks for Crowd Counting,
CirSysVideo(30), No. 9, September 2020, pp. 3119-3129.
IEEE DOI
2009
Estimation, Neural networks, Training, Image segmentation,
Feature extraction, Head, Videos, Crowd counting, mask-aware network,
regression
BibRef
Wu, X.J.[Xing-Jiao],
Kong, S.C.[Shu-Chen],
Zheng, Y.B.[Ying-Bin],
Ye, H.[Hao],
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Feature channel enhancement for crowd counting,
IET-IPR(14), No. 11, September 2020, pp. 2376-2382.
DOI Link
2009
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Lei, Y.J.[Yin-Jie],
Liu, Y.[Yan],
Zhang, P.P.[Ping-Ping],
Liu, L.Q.[Ling-Qiao],
Towards using count-level weak supervision for crowd counting,
PR(109), 2021, pp. 107616.
Elsevier DOI
2009
Crowd counting, Count-level annotation, Weak supervision,
Auxiliary tasks learning, Asymmetry training
BibRef
Gao, J.,
Wang, Q.,
Li, X.,
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network,
CirSysVideo(30), No. 10, October 2020, pp. 3486-3498.
IEEE DOI
2010
Estimation, Feature extraction, Image segmentation, Training,
Task analysis, Head, Semantics, Crowd counting, crowd analysis,
multi-task learning
BibRef
Sajid, U.,
Sajid, H.,
Wang, H.,
Wang, G.,
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images,
CirSysVideo(30), No. 10, October 2020, pp. 3499-3512.
IEEE DOI
2010
Estimation, Computational modeling, Benchmark testing, Head,
Training, Routing, Crowd counting, crowd density,
zooming or normal patch-making blocks
BibRef
Zhao, M.M.[Mu-Ming],
Zhang, C.Y.[Chong-Yang],
Zhang, J.[Jian],
Porikli, F.M.[Fatih M.],
Ni, B.B.[Bing-Bing],
Zhang, W.J.[Wen-Jun],
Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural
Networks,
CirSysVideo(30), No. 10, October 2020, pp. 3651-3662.
IEEE DOI
2010
Estimation, Distortion, Cameras, Task analysis,
Convolutional neural networks, Australia, Fuses, Crowd counting,
scale variation
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Yilmaz, B.[Bedir],
Sheikh Abdullah, S.N.H.[Siti Norul Huda],
Kok, V.J.[Ven Jyn],
Vanishing region loss for crowd density estimation,
PRL(138), 2020, pp. 336-345.
Elsevier DOI
2010
Crowd counting, Crowd density estimation,
Perspective distortion, Crowd analysis, Auxiliary loss
BibRef
Wang, S.[Suyu],
Yang, B.[Bin],
Liu, B.[Bo],
Zheng, G.H.[Guang-Hui],
Dual attention module and multi-label based fully convolutional network
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IET-CV(14), No. 7, October 2020, pp. 443-451.
DOI Link
2010
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Hacar, Ö.Ö.[Özge Öztürk],
Gülgen, F.[Fatih],
Bilgi, S.[Serdar],
Evaluation of the Space Syntax Measures Affecting Pedestrian Density
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IJGI(9), No. 10, 2020, pp. xx-yy.
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2010
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Li, H.[He],
Kong, W.H.[Wei-Hang],
Zhang, S.H.[Shi-Hui],
Effective crowd counting using multi-resolution context and image
quality assessment-guided training,
CVIU(201), 2020, pp. 103065.
Elsevier DOI
2011
Crowd counting, Scale variant, Image quality assessment, Multi-resolution
BibRef
Cao, Z.J.[Zhi-Jie],
Shamsolmoali, P.[Pourya],
Yang, J.[Jie],
Synthetic guided domain adaptive and edge aware network for crowd
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IVC(104), 2020, pp. 104026.
Elsevier DOI
2012
Crowd counintg, Synthetic guided, Edge aware, Domain adaption
BibRef
Jiang, X.O.[Xia-Oheng],
Zhang, L.[Li],
Zhang, T.Z.[Tian-Zhu],
Lv, P.[Pei],
Zhou, B.[Bing],
Pang, Y.W.[Yan-Wei],
Xu, M.L.[Ming-Liang],
Xu, C.S.[Chang-Sheng],
Density-Aware Multi-Task Learning for Crowd Counting,
MultMed(23), 2021, pp. 443-453.
IEEE DOI
2012
Task analysis, Semantics, Estimation, Feature extraction,
Convolutional neural networks, Cameras, Head, multi-task learning
BibRef
Ren, W.,
Wang, X.,
Tian, J.,
Tang, Y.,
Chan, A.B.,
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for
Tracking Multiple Targets,
IP(30), 2021, pp. 1439-1452.
IEEE DOI
2012
Target tracking, Tracking, Object detection, Trajectory, Detectors,
Task analysis, Computational modeling, People tracking,
flow tracking
BibRef
Yang, Y.,
Li, G.,
Du, D.,
Huang, Q.,
Sebe, N.,
Embedding Perspective Analysis Into Multi-Column Convolutional Neural
Network for Crowd Counting,
IP(30), 2021, pp. 1395-1407.
IEEE DOI
2012
Convolution, Estimation, Transforms, Kernel, Training, Standards,
Smoothing methods, Crowd counting, multi-column network,
transform dilated convolution
BibRef
Sindagi, V.A.[Vishwanath A.],
Yasarla, R.[Rajeev],
Babu, D.S.[Deepak Sam],
Babu, R.V.[R. Venkatesh],
Patel, V.M.[Vishal M.],
Learning to Count in the Crowd from Limited Labeled Data,
ECCV20(XI:212-229).
Springer DOI
2011
BibRef
Liu, Y.[Yan],
Liu, L.Q.[Ling-Qiao],
Wang, P.[Peng],
Zhang, P.P.[Ping-Ping],
Lei, Y.[Yinjie],
Semi-Supervised Crowd Counting via Self-training on Surrogate Tasks,
ECCV20(XV:242-259).
Springer DOI
2011
BibRef
Liu, W.Z.[Wei-Zhe],
Salzmann, M.[Mathieu],
Fua, P.[Pascal],
Estimating People Flows to Better Count Them in Crowded Scenes,
ECCV20(XV:723-740).
Springer DOI
2011
BibRef
Wu, Q.,
Zhang, C.,
Kong, X.,
Zhao, M.,
Chen, Y.,
Triple Attention For Robust Video Crowd Counting,
ICIP20(1966-1970)
IEEE DOI
2011
TV, Facsimile, Crowd counting, Co-attention, Robustness
BibRef
Xie, Y.,
Lu, Y.,
Wang, S.,
RSANet: Deep Recurrent Scale-Aware Network for Crowd Counting,
ICIP20(1531-1535)
IEEE DOI
2011
Convolution, Image restoration, Training, Task analysis, Decoding,
Computer vision, Robustness, Crowd counting, Recurrent network
BibRef
Shim, K.,
Byun, J.,
Kim, C.,
Multi-Step Quantization Of A Multi-Scale Network For Crowd Counting,
ICIP20(683-687)
IEEE DOI
2011
Quantization (signal), Training, Decoding, Visualization,
Surveillance, Kernel, Head, Crowd counting,
Crowd density estimation, Quantization
BibRef
Zhao, Z.[Zhen],
Shi, M.J.[Miao-Jing],
Zhao, X.X.[Xiao-Xiao],
Li, L.[Li],
Active Crowd Counting with Limited Supervision,
ECCV20(XX:565-581).
Springer DOI
2011
BibRef
Liu, L.[Liang],
Lu, H.[Hao],
Zou, H.W.[Hong-Wei],
Xiong, H.P.[Hai-Peng],
Cao, Z.G.[Zhi-Guo],
Shen, C.H.[Chun-Hua],
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning,
ECCV20(X:164-181).
Springer DOI
2011
BibRef
Yang, Y.F.[Yi-Fan],
Li, G.R.[Guo-Rong],
Wu, Z.[Zhe],
Su, L.[Li],
Huang, Q.M.[Qing-Ming],
Sebe, N.[Nicu],
Weakly-supervised Crowd Counting Learns from Sorting Rather Than
Locations,
ECCV20(VIII:1-17).
Springer DOI
2011
BibRef
Jiang, X.,
Zhang, L.,
Xu, M.,
Zhang, T.,
Lv, P.,
Zhou, B.,
Yang, X.,
Pang, Y.,
Attention Scaling for Crowd Counting,
CVPR20(4705-4714)
IEEE DOI
2008
Task analysis, Estimation, Kernel, Training,
Convolutional neural networks, Feature extraction, Computer vision
BibRef
Reddy, M.K.K.[M. K. Krishna],
Hossain, M.A.[M. Asiful],
Rochan, M.,
Wang, Y.,
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning,
WACV20(2803-2812)
IEEE DOI
2006
Adaptation models, Training, Cameras, Task analysis, Data models,
Surveillance, Training data
BibRef
Sajid, U.,
Wang, G.,
Plug-and-Play Rescaling Based Crowd Counting in Static Images,
WACV20(2276-2285)
IEEE DOI
2006
Estimation, Switches, Detectors, Computer architecture,
Image recognition, Benchmark testing, Measurement
BibRef
Hossain, M.A.,
Reddy, M.K.K.,
Cannons, K.,
Xu, Z.,
Wang, Y.,
Domain Adaptation in Crowd Counting,
CRV20(150-157)
IEEE DOI
2006
domain adaptation, crowd counting, few-shot learning, density map
BibRef
Phan, C.,
Hoang, A.,
Phan, D.,
Dao, H.,
Huynh, V.,
Human Density Estimation by Exploiting Deep Spatial Contextual
Information,
IVCNZ19(1-5)
IEEE DOI
2004
convolutional neural nets, feature extraction, image capture,
image classification, learning (artificial intelligence),
Long Short-Term Memory (LSTM)
BibRef
Xu, C.,
Qiu, K.,
Fu, J.,
Bai, S.,
Xu, Y.,
Bai, X.,
Learn to Scale:
Generating Multipolar Normalized Density Maps for Crowd Counting,
ICCV19(8381-8389)
IEEE DOI
2004
image motion analysis, image resolution,
learning (artificial intelligence),
BibRef
Zhang, A.,
Shen, J.,
Xiao, Z.,
Zhu, F.,
Zhen, X.,
Cao, X.,
Shao, L.,
Relational Attention Network for Crowd Counting,
ICCV19(6787-6796)
IEEE DOI
2004
computer vision, image fusion, image representation,
learning (artificial intelligence), neural nets, Image reconstruction
BibRef
Cheng, Z.,
Li, J.,
Dai, Q.,
Wu, X.,
Hauptmann, A.G.[Alexander G.],
Learning Spatial Awareness to Improve Crowd Counting,
ICCV19(6151-6160)
IEEE DOI
2004
convolutional neural nets, gradient methods,
learning (artificial intelligence), head size changes,
Benchmark testing
BibRef
Liu, J.,
Gao, C.,
Meng, D.,
Hauptmann, A.G.,
DecideNet: Counting Varying Density Crowds Through Attention Guided
Detection and Density Estimation,
CVPR18(5197-5206)
IEEE DOI
1812
Estimation, Detectors, Reliability, Head, Computer vision,
Task analysis, Visualization
BibRef
Ma, Z.,
Wei, X.,
Hong, X.,
Gong, Y.,
Bayesian Loss for Crowd Count Estimation With Point Supervision,
ICCV19(6141-6150)
IEEE DOI
2004
Bayes methods, learning (artificial intelligence),
object detection, probability, Feature extraction
BibRef
Zhang, A.,
Yue, L.,
Shen, J.,
Zhu, F.,
Zhen, X.,
Cao, X.,
Shao, L.,
Attentional Neural Fields for Crowd Counting,
ICCV19(5713-5722)
IEEE DOI
2004
computer vision, feature extraction, image representation,
object detection, random processes, nonlocal attention mechanism,
Machine learning
BibRef
Yan, Z.,
Yuan, Y.,
Zuo, W.,
Tan, X.,
Wang, Y.,
Wen, S.,
Ding, E.,
Perspective-Guided Convolution Networks for Crowd Counting,
ICCV19(952-961)
IEEE DOI
2004
Code, Convolutional Networks.
WWW Link. convolutional neural nets, image resolution, object detection,
perspective-guided convolution networks,
Benchmark testing
BibRef
Sindagi, V.,
Patel, V.,
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd
Counting,
ICCV19(1002-1012)
IEEE DOI
2004
feature extraction, image classification, image fusion,
multiscale fusion, scale-aware ground-truth density maps,
Training
BibRef
Wan, J.,
Chan, A.,
Adaptive Density Map Generation for Crowd Counting,
ICCV19(1130-1139)
IEEE DOI
2004
computer vision, estimation theory, feature extraction,
learning (artificial intelligence), neural nets,
Feature extraction
BibRef
Sindagi, V.,
Yasarla, R.,
Patel, V.,
Pushing the Frontiers of Unconstrained Crowd Counting:
New Dataset and Benchmark Method,
ICCV19(1221-1231)
IEEE DOI
2004
Dataset, Crowd Counting. feature extraction, image classification,
learning (artificial intelligence), object detection, Error analysis
BibRef
Liu, L.,
Qiu, Z.,
Li, G.,
Liu, S.,
Ouyang, W.,
Lin, L.,
Crowd Counting With Deep Structured Scale Integration Network,
ICCV19(1774-1783)
IEEE DOI
2004
feature extraction, image enhancement, image fusion,
image representation, learning (artificial intelligence), Head
BibRef
Yang, S.,
Su, H.,
Hsu, W.H.,
Chen, W.,
DECCNet: Depth Enhanced Crowd Counting,
CroMoL19(4521-4530)
IEEE DOI
2004
feature extraction, image colour analysis,
learning (artificial intelligence), neural nets,
RGBD
BibRef
Bai, H.,
Wen, S.,
Chan, S.G.,
Crowd Counting on Images with Scale Variation and Isolated Clusters,
VisDrone19(18-27)
IEEE DOI
2004
feature extraction, image classification, image segmentation,
object detection, pattern clustering,
Isolated Clusters
BibRef
Wan, J.[Jia],
Luo, W.[Wenhan],
Wu, B.Y.[Bao-Yuan],
Chan, A.B.[Antoni B.],
Liu, W.[Wei],
Residual Regression With Semantic Prior for Crowd Counting,
CVPR19(4031-4040).
IEEE DOI
2002
BibRef
Lian, D.Z.[Dong-Ze],
Li, J.[Jing],
Zheng, J.[Jia],
Luo, W.X.[Wei-Xin],
Gao, S.H.[Sheng-Hua],
Density Map Regression Guided Detection Network for RGB-D Crowd
Counting and Localization,
CVPR19(1821-1830).
IEEE DOI
2002
BibRef
Liu, C.C.[Chen-Chen],
Weng, X.Y.[Xin-Yu],
Mu, Y.D.[Ya-Dong],
Recurrent Attentive Zooming for Joint Crowd Counting and Precise
Localization,
CVPR19(1217-1226).
IEEE DOI
2002
BibRef
Liu, W.[Weizhe],
Salzmann, M.[Mathieu],
Fua, P.[Pascal],
Context-Aware Crowd Counting,
CVPR19(5094-5103).
IEEE DOI
2002
BibRef
Jiang, X.L.[Xiao-Long],
Xiao, Z.[Zehao],
Zhang, B.C.[Bao-Chang],
Zhen, X.T.[Xian-Tong],
Cao, X.B.[Xian-Bin],
Doermann, D.[David],
Shao, L.[Ling],
Crowd Counting and Density Estimation by Trellis Encoder-Decoder
Networks,
CVPR19(6126-6135).
IEEE DOI
2002
BibRef
Liu, Y.[Yuting],
Shi, M.[Miaojing],
Zhao, Q.[Qijun],
Wang, X.F.[Xiao-Fang],
Point in, Box Out: Beyond Counting Persons in Crowds,
CVPR19(6462-6471).
IEEE DOI
2002
BibRef
Shi, M.[Miaojing],
Yang, Z.H.[Zhao-Hui],
Xu, C.[Chao],
Chen, Q.[Qijun],
Revisiting Perspective Information for Efficient Crowd Counting,
CVPR19(7271-7280).
IEEE DOI
2002
BibRef
Wang, Q.[Qi],
Gao, J.Y.[Jun-Yu],
Lin, W.[Wei],
Yuan, Y.[Yuan],
Learning From Synthetic Data for Crowd Counting in the Wild,
CVPR19(8190-8199).
IEEE DOI
2002
BibRef
Zhang, Q.[Qi],
Chan, A.B.[Antoni B.],
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View
Fusion CNNs,
CVPR19(8289-8298).
IEEE DOI
2002
BibRef
Yilmaz, B.,
Kok, V.J.,
Lim, M.K.,
Abdullah, S.N.H.S.,
Perspective-Aware Loss Function for Crowd Density Estimation,
MVA19(1-6)
DOI Link
1911
convolutional neural nets, estimation theory, image processing,
learning (artificial intelligence), perspective distortion, Layout
BibRef
Khan, S.D.,
Ullah, H.,
Uzair, M.,
Ullah, M.,
Ullah, R.,
Cheikh, F.A.,
Disam: Density Independent and Scale Aware Model for Crowd Counting
and Localization,
ICIP19(4474-4478)
IEEE DOI
1910
Crowd counting, Convolution networks, Head detection, Classification
BibRef
Vandoni, J.,
Aldea, E.,
Hégarat-Mascle, S.L.,
Evaluating Crowd Density Estimators Via Their Uncertainty Bounds,
ICIP19(4579-4583)
IEEE DOI
1910
density estimation, crowd counting, multi-scale evaluation, uncertainty bounds
BibRef
Huynh, V.,
Tran, V.,
Huang, C.,
DAnet: Depth-Aware Network for Crowd Counting,
ICIP19(3001-3005)
IEEE DOI
1910
Deep learning, crowd counting, depth estimation, multi-task learning
BibRef
Zhao, K.,
Liu, B.,
Song, L.,
Li, W.,
Yu, N.,
Cascaded Residual Density Network for Crowd Counting,
ICIP19(2199-2203)
IEEE DOI
1910
Crowd counting, Scale variation, CRD-Net, Local count loss
BibRef
Rodriguez, A.C.[Andres C.],
Wegner, J.D.[Jan D.],
Counting the Uncountable: Deep Semantic Density Estimation from Space,
GCPR18(351-362).
Springer DOI
1905
BibRef
Hossain, M.,
Hosseinzadeh, M.,
Chanda, O.,
Wang, Y.,
Crowd Counting Using Scale-Aware Attention Networks,
WACV19(1280-1288)
IEEE DOI
1904
learning (artificial intelligence), neural net architecture,
object detection, crowded scene, crowd density, crowd counting,
Computational modeling
BibRef
Chen, X.,
Bin, Y.,
Sang, N.,
Gao, C.,
Scale Pyramid Network for Crowd Counting,
WACV19(1941-1950)
IEEE DOI
1904
computer vision, object detection, pedestrians,
traffic engineering computing, Scale Pyramid Module,
Computer vision
BibRef
Shen, W.,
Qin, P.,
Zeng, J.,
An Indoor Crowd Detection Network Framework Based on Feature
Aggregation Module and Hybrid Attention Selection Module,
VisDrone19(82-90)
IEEE DOI
2004
feature extraction, image fusion, object detection,
indoor crowd detection network framework, Feature aggregation
BibRef
Liciotti, D.,
Paolanti, M.,
Pietrini, R.,
Frontoni, E.,
Zingaretti, P.,
Convolutional Networks for Semantic Heads Segmentation using Top-View
Depth Data in Crowded Environment,
ICPR18(1384-1389)
IEEE DOI
1812
Image segmentation, Semantics, Cameras, Fractals, Head,
Computer architecture, Training
BibRef
Ren, W.,
Kang, D.,
Tang, Y.,
Chan, A.B.,
Fusing Crowd Density Maps and Visual Object Trackers for People
Tracking in Crowd Scenes,
CVPR18(5353-5362)
IEEE DOI
1812
Target tracking, Visualization, Correlation, Estimation,
Adaptation models, Object detection, Lighting
BibRef
Deb, D.,
Ventura, J.,
An Aggregated Multicolumn Dilated Convolution Network for
Perspective-Free Counting,
Crowd18(308-30809)
IEEE DOI
1812
Convolution, Feature extraction, Training, Aggregates, Kernel,
Data mining, Convolutional neural networks
BibRef
Jeong, J.,
Jeong, H.,
Lim, J.,
Choi, J.,
Yun, S.,
Choi, J.Y.,
Selective Ensemble Network for Accurate Crowd Density Estimation,
ICPR18(320-325)
IEEE DOI
1812
Training, Estimation, Feature extraction, Image resolution,
Network architecture, Surveillance, Cameras
BibRef
Saqib, M.,
Daud Khan, S.,
Blumenstein, M.,
Texture-based feature mining for crowd density estimation: A study,
ICVNZ16(1-6)
IEEE DOI
1701
Cameras
BibRef
Xu, B.,
Qiu, G.,
Crowd density estimation based on rich features and random projection
forest,
WACV16(1-8)
IEEE DOI
1606
Computational modeling
BibRef
Kaminski, L.,
Gardzinski, P.,
Kowalak, K.,
Mackowiak, S.,
Unsupervised abnormal crowd activity detection in surveillance
systems,
WSSIP16(1-4)
IEEE DOI
1608
BibRef
Earlier: A2, A3, A1, A4:
Crowd density estimation based on voxel model in multi-view
surveillance systems,
WSSIP15(216-219)
IEEE DOI
1603
image classification
BibRef
Pham, V.Q.,
Kozakaya, T.,
Yamaguchi, O.,
Okada, R.,
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random
Forest for Crowd Density Estimation,
ICCV15(3253-3261)
IEEE DOI
1602
Computational modeling
BibRef
Zhang, Y.Y.[Ying-Ying],
Zhou, D.[Desen],
Chen, S.Q.[Si-Qin],
Gao, S.H.[Sheng-Hua],
Ma, Y.[Yi],
Single-Image Crowd Counting via Multi-Column Convolutional Neural
Network,
CVPR16(589-597)
IEEE DOI
1612
BibRef
Shi, Z.,
Zhang, L.,
Liu, Y.,
Cao, X.,
Ye, Y.,
Cheng, M.,
Zheng, G.,
Crowd Counting with Deep Negative Correlation Learning,
CVPR18(5382-5390)
IEEE DOI
1812
Training, Correlation, Computer vision, Decorrelation, Testing,
Complexity theory, Visualization
BibRef
Shen, Z.,
Xu, Y.,
Ni, B.,
Wang, M.,
Hu, J.,
Yang, X.,
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit,
CVPR18(5245-5254)
IEEE DOI
1812
Estimation, Feature extraction, Training, Task analysis, Kernel,
Generators, Switches
BibRef
Marsden, M.,
McGuinness, K.,
Little, S.,
Keogh, C.E.,
O'Connor, N.E.,
People, Penguins and Petri Dishes: Adapting Object Counting Models to
New Visual Domains and Object Types Without Forgetting,
CVPR18(8070-8079)
IEEE DOI
1812
Visualization, Task analysis, Training, Adaptation models, Wildlife,
Computer architecture, Convolutional neural networks
BibRef
Liu, X.,
van de Weijer, J.,
Bagdanov, A.D.,
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank,
CVPR18(7661-7669)
IEEE DOI
1812
Task analysis, Training, Computer vision, Visualization, Estimation,
Head, Context modeling
BibRef
Yang, J.,
Zhou, Y.,
Kung, S.,
Multi-scale Generative Adversarial Networks for Crowd Counting,
ICPR18(3244-3249)
IEEE DOI
1812
Generators, Feature extraction, Estimation, Convolution,
Generative adversarial networks, Task analysis, Training
BibRef
Idrees, H.[Haroon],
Tayyab, M.[Muhmmad],
Athrey, K.[Kishan],
Zhang, D.[Dong],
Al-Maadeed, S.[Somaya],
Rajpoot, N.[Nasir],
Shah, M.[Mubarak],
Composition Loss for Counting, Density Map Estimation and Localization
in Dense Crowds,
ECCV18(II: 544-559).
Springer DOI
1810
BibRef
Laradji, I.H.[Issam H.],
Rostamzadeh, N.[Negar],
Pinheiro, P.O.[Pedro O.],
Vazquez, D.[David],
Schmidt, M.[Mark],
Where Are the Blobs: Counting by Localization with Point Supervision,
ECCV18(II: 560-576).
Springer DOI
1810
BibRef
Ranjan, V.[Viresh],
Le, H.[Hieu],
Hoai, M.[Minh],
Iterative Crowd Counting,
ECCV18(VII: 278-293).
Springer DOI
1810
BibRef
Cao, X.K.[Xin-Kun],
Wang, Z.P.[Zhi-Peng],
Zhao, Y.Y.[Yan-Yun],
Su, F.[Fei],
Scale Aggregation Network for Accurate and Efficient Crowd Counting,
ECCV18(VI: 757-773).
Springer DOI
1810
BibRef
Amirgholipour, S.,
He, X.,
Jia, W.,
Wang, D.,
Zeibots, M.,
A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting,
ICIP18(948-952)
IEEE DOI
1809
Head, Adaptation models, Training, Linguistics, Estimation, Testing,
Australia, Crowd counting, Scale Variation, Adaptive Counting CNN
BibRef
Liu, M.,
Liu, Y.,
Jiang, J.,
Guo, Z.,
Wang, Z.,
Crowd Counting with Fully Convolutional Neural Network,
ICIP18(953-957)
IEEE DOI
1809
Estimation, Testing, Training, Kernel, Feature extraction,
Convolutional neural networks, Task analysis, Crowd counting,
deep learning
BibRef
Küchhold, M.,
Simon, M.,
Eiselein, V.,
Sikora, T.,
Scale-Adaptive Real-Time Crowd Detection and Counting for Drone
Images,
ICIP18(943-947)
IEEE DOI
1809
Image segmentation, Drones, Feature extraction, Image resolution,
Cameras, Real-time systems, Kernel, crowd counting, crowd detection,
surveillance
BibRef
Cao, J.M.[Jin-Meng],
Yang, B.[Biao],
Zhang, Y.Y.[Yu-Yu],
Zou, L.[Ling],
Crowd Counting from a Still Image Using Multi-scale Fully Convolutional
Network with Adaptive Human-Shaped Kernel,
PSIVTWS17(227-240).
Springer DOI
1806
BibRef
Pai, A.K.,
Karunakar, A.K.,
Raghavendra, U.,
A Novel Crowd Density Estimation Technique using Local Binary Pattern
and Gabor Features,
AVSS17(1-6)
IEEE DOI
1806
Gabor filters, computer vision, feature extraction,
image representation, image texture, pattern classification,
Video surveillance
BibRef
Vandoni, J.,
Aldea, E.,
Le Hégarat-Mascle, S.,
Active learning for high-density crowd count regression,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, image recognition,
learning (artificial intelligence), object detection,
Training
BibRef
Jiang, H.,
Jin, W.,
Yu, Z.,
Xu, P.,
Combing spatial and temporal features for crowd counting with point
supervision,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, image motion analysis, object detection,
video signal processing, crowd counting map, crowd density map,
Vegetation
BibRef
Sindagi, V.A.[Vishwanath A.],
Patel, V.M.[Vishal M.],
CNN-Based cascaded multi-task learning of high-level prior and
density estimation for crowd counting,
AVSS17(1-6)
IEEE DOI
1806
convolution, image classification,
learning (artificial intelligence), neural nets,
Training
BibRef
Marsden, M.,
McGuinness, K.,
Little, S.,
O'Connor, N.E.,
ResnetCrowd: A residual deep learning architecture for crowd
counting, violent behaviour detection and crowd density level
classification,
AVSS17(1-7)
IEEE DOI
1806
computer vision, estimation theory, feature extraction,
image classification, learning (artificial intelligence),
Urban areas
BibRef
Fan, C.,
Tang, J.,
Wang, N.,
Liang, D.,
Rich Convolutional Features Fusion for Crowd Counting,
FG18(394-398)
IEEE DOI
1806
Computer architecture, Estimation, Feature extraction,
Heating systems, Robustness, Task analysis, Training, CNN,
features fusion
BibRef
Zhang, L.,
Shi, M.,
Chen, Q.,
Crowd Counting via Scale-Adaptive Convolutional Neural Network,
WACV18(1113-1121)
IEEE DOI
1806
computer vision, feature extraction, image classification,
learning (artificial intelligence), neural nets,
Training
BibRef
Olmschenk, G.,
Tang, H.,
Zhu, Z.,
Crowd Counting with Minimal Data Using Generative Adversarial
Networks for Multiple Target Regression,
WACV18(1151-1159)
IEEE DOI
1806
feedforward neural nets, inference mechanisms,
learning (artificial intelligence), object recognition,
Training
BibRef
Zeng, L.,
Xu, X.,
Cai, B.,
Qiu, S.,
Zhang, T.,
Multi-scale convolutional neural networks for crowd counting,
ICIP17(465-469)
IEEE DOI
1803
Convolutional neural networks, Feature extraction, Kernel,
Optimization, Robustness, Training, Multi-scale CNN, crowd counting,
scale-relevant architectures
BibRef
Xiong, F.,
Shi, X.,
Yeung, D.Y.,
Spatiotemporal Modeling for Crowd Counting in Videos,
ICCV17(5161-5169)
IEEE DOI
1802
image sequences, learning (artificial intelligence), neural nets,
regression analysis, video signal processing, CNN,
Videos
BibRef
Sam, D.B.,
Surya, S.,
Babu, R.V.,
Switching Convolutional Neural Network for Crowd Counting,
CVPR17(4031-4039)
IEEE DOI
1711
Computer architecture, Head, Neural networks,
Relays, Switches, Training
BibRef
Wang, T.[Tao],
Li, G.[Guohui],
Lei, J.[Jun],
Li, S.[Shuohao],
Xu, S.[Shukui],
Crowd Counting Based on MMCNN in Still Images,
SCIA17(I: 468-479).
Springer DOI
1706
BibRef
Elassal, N.[Nada],
Elder, J.H.[James H.],
Unsupervised Crowd Counting,
ACCV16(V: 329-345).
Springer DOI
1704
BibRef
Siva, P.,
Shafiee, M.J.,
Jamieson, M.,
Wong, A.,
Real-Time, Embedded Scene Invariant Crowd Counting Using
Scale-Normalized Histogram of Moving Gradients (HoMG),
ECVW16(885-892)
IEEE DOI
1612
BibRef
Shang, C.,
Ai, H.,
Bai, B.,
End-to-end crowd counting via joint learning local and global count,
ICIP16(1215-1219)
IEEE DOI
1610
Computational modeling
BibRef
Zalluhoglu, C.[Cemil],
Ikizler-Cinbis, N.[Nazli],
Counting People in Crowded Scenes via Detection and Regression Fusion,
ICIAR16(309-317).
Springer DOI
1608
BibRef
Khan, U.[Usman],
Klette, R.[Reinhard],
Logarithmically Improved Property Regression for Crowd Counting,
PSIVT15(123-135).
Springer DOI
1602
BibRef
Yang, R.[Ren],
Xu, H.Z.[Hua-Zhong],
Wang, J.Q.[Jin-Qiao],
Robust Crowd Segmentation and Counting in Indoor Scenes,
MMMod16(I: 505-514).
Springer DOI
1601
BibRef
Zhao, Z.[Zhuoyi],
Li, H.S.[Hong-Sheng],
Zhao, R.[Rui],
Wang, X.G.[Xiao-Gang],
Crossing-Line Crowd Counting with Two-Phase Deep Neural Networks,
ECCV16(VIII: 712-726).
Springer DOI
1611
BibRef
Zhang, C.[Cong],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Yang, X.K.[Xiao-Kang],
Cross-scene crowd counting via deep convolutional neural networks,
CVPR15(833-841)
IEEE DOI
1510
BibRef
Kumagai, S.[Shohei],
Hotta, K.[Kazuhiro],
HLAC between Cells of HOG Feature for Crowd Counting,
ISVC14(I: 688-697).
Springer DOI
1501
BibRef
Pedersen, J.B.,
Markussen, J.B.,
Philipsen, M.P.,
Jensen, M.B.,
Moeslund, T.B.,
Counting the Crowd at a Carnival,
ISVC14(II: 706-715).
Springer DOI
1501
BibRef
Bondi, E.[Enrico],
Seidenari, L.[Lorenzo],
Bagdanov, A.D.[Andrew D.],
del Bimbo, A.[Alberto],
Real-time people counting from depth imagery of crowded environments,
AVSS14(337-342)
IEEE DOI
1411
Cameras
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Fradi, H.[Hajer],
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Human detection in a challenging situation,
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Earlier:
Estimating the number of people in crowded scenes by MID based
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0812
See also Robust automated ground plane rectification based on moving vehicles for traffic scene surveillance.
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HTML Version.
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BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Crosswalk Detection, Zebra Crossings .