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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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1808
Semantics, Feature extraction, Image representation, Encoding, Roads,
Neural networks, Image segmentation, Crowd counting,
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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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Cross-Line Pedestrian Counting Based on Spatially-Consistent
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1903
Estimation, Feature extraction, Scalability, Reliability, Cameras,
Head, Support vector machines, Pedestrian counting,
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1905
Training, Task analysis, Image sequences, Redundancy,
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Kang, D.,
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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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1909
Crowd counting, Spatio-temporal feature, Crowd analysis
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Xu, M.L.[Ming-Liang],
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1909
Crowd counting, Depth information, Pedestrian detection, Density estimation
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Shami, M.B.,
Maqbool, S.,
Sajid, H.,
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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,
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Sindagi, V.A.,
Patel, V.M.,
HA-CCN: Hierarchical Attention-Based Crowd Counting Network,
IP(29), 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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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,
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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.
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2003
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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.
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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.T.[Yong-Tuo],
Wen, Q.[Qiang],
Chen, H.X.[Hao-Xin],
Liu, W.X.[Wen-Xi],
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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
Chai, L.Y.[Liang-Yu],
Liu, Y.T.[Yong-Tuo],
Liu, W.X.[Wen-Xi],
Han, G.Q.[Guo-Qiang],
He, S.F.[Sheng-Feng],
CrowdGAN: Identity-Free Interactive Crowd Video Generation and Beyond,
PAMI(44), No. 6, June 2022, pp. 2856-2871.
IEEE DOI
2205
Trajectory, Task analysis,
Predictive models, Analytical models, Uncertainty, Solid modeling,
crowd analysis
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],
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Feature channel enhancement for crowd counting,
IET-IPR(14), No. 11, September 2020, pp. 2376-2382.
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2009
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Lei, Y.J.[Yin-Jie],
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Zhang, P.P.[Ping-Ping],
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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.,
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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
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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
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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
BibRef
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],
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Effective crowd counting using multi-resolution context and image
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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
Bai, L.[Liu],
Wu, C.[Cheng],
Xie, F.[Feng],
Wang, Y.M.[Yi-Ming],
Crowd density detection method based on crowd gathering mode and
multi-column convolutional neural network,
IVC(105), 2021, pp. 104084.
Elsevier DOI
2101
Overcrowding, Crowd gathering safety,
Video surveillance, Accident analysis and early warning
BibRef
Wang, Q.[Qi],
Gao, J.Y.[Jun-Yu],
Lin, W.[Wei],
Yuan, Y.[Yuan],
Pixel-Wise Crowd Understanding via Synthetic Data,
IJCV(129), No. 1, January 2021, pp. 225-245.
Springer DOI
2101
BibRef
Earlier:
Learning From Synthetic Data for Crowd Counting in the Wild,
CVPR19(8190-8199).
IEEE DOI
2002
BibRef
Wu, Y.[Yue],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
Learning From Synthetic Data for Crowd Instance Segmentation in the
Wild,
ICIP22(2391-2395)
IEEE DOI
2211
Image segmentation, Adaptation models, Codes, Video surveillance,
Data models, Generators, Task analysis,
domain adaption
BibRef
Wang, Y.,
Hou, J.,
Hou, X.,
Chau, L.P.,
A Self-Training Approach for Point-Supervised Object Detection and
Counting in Crowds,
IP(30), 2021, pp. 2876-2887.
IEEE DOI
2102
Detectors, Training, Annotations, Object detection, Decoding,
Feature extraction, Location awareness, weak supervision
BibRef
Cheng, J.,
Xiong, H.,
Cao, Z.,
Lu, H.,
Decoupled Two-Stage Crowd Counting and Beyond,
IP(30), 2021, pp. 2862-2875.
IEEE DOI
2102
Location awareness, Probabilistic logic, Training, Reliability,
Object recognition, Kernel, Detection algorithms, Crowd counting,
local count models
BibRef
Xue, Y.,
Li, Y.,
Liu, S.,
Zhang, X.,
Qian, X.,
Crowd Scene Analysis Encounters High Density and Scale Variation,
IP(30), 2021, pp. 2745-2757.
IEEE DOI
2102
Location awareness, Image reconstruction, Image coding,
Task analysis, Training, Compressed sensing, Head, Crowd counting,
crowd localization
BibRef
Wan, J.,
Kumar, N.S.,
Chan, A.B.,
Fine-Grained Crowd Counting,
IP(30), 2021, pp. 2114-2126.
IEEE DOI
2102
image segmentation, pose estimation, video signal processing,
video surveillance, fine-grained crowd counting,
fine-grained crowd counting
BibRef
Liu, L.,
Jiang, J.,
Jia, W.,
Amirgholipour, S.,
Wang, Y.,
Zeibots, M.,
He, X.,
DENet: A Universal Network for Counting Crowd With Varying Densities
and Scales,
MultMed(23), 2021, pp. 1060-1068.
IEEE DOI
2103
Convolution, Estimation, Feature extraction, Loss measurement,
Image segmentation,
detection
BibRef
Wang, Y.J.[Yong-Jie],
Zhang, W.[Wei],
Huang, D.X.[Dong-Xiao],
Liu, Y.Y.[Yan-Yan],
Zhu, J.H.[Jiang-Hua],
Multi-scale supervised network for crowd counting,
IET-IPR(14), No. 17, 24 December 2020, pp. 4701-4707.
DOI Link
2104
BibRef
Wang, Q.[Qi],
Gao, J.Y.[Jun-Yu],
Lin, W.[Wei],
Li, X.L.[Xue-Long],
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and
Localization,
PAMI(43), No. 6, June 2021, pp. 2141-2149.
IEEE DOI
WWW Link.
WWW Link.
2106
Dataset, Crowd Counting. Benchmark testing, Task analysis, Head, Surveillance, Cameras,
Magnetic heads, Internet, Crowd counting, crowd localization,
benchmark website
BibRef
Chen, J.[Jiwei],
Wang, Z.F.[Zeng-Fu],
Crowd counting with segmentation attention convolutional neural
network,
IET-IPR(15), No. 6, 2021, pp. 1221-1231.
DOI Link
2106
BibRef
Xia, Y.F.[Yin-Feng],
He, Y.Q.[Yu-Qiang],
Peng, S.[Sifan],
Yang, Q.Q.[Qian-Qian],
Yin, B.Q.[Bao-Qun],
CFFNet: Coordinated feature fusion network for crowd counting,
IVC(112), 2021, pp. 104242.
Elsevier DOI
2107
Crowd counting, Feature fusion, Spatial alignment, Semantic consistency
BibRef
Sam, D.B.[Deepak Babu],
Peri, S.V.[Skand Vishwanath],
Sundararaman, M.N.[Mukuntha Narayanan],
Kamath, A.[Amogh],
Babu, R.V.[R. Venkatesh],
Locate, Size, and Count: Accurately Resolving People in Dense Crowds
via Detection,
PAMI(43), No. 8, August 2021, pp. 2739-2751.
IEEE DOI
2107
Training, Detectors, Magnetic heads, Face, Feature extraction,
Task analysis, Crowd counting, head detection, deep learning
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
Sam, D.B.,
Surya, S.,
Babu, R.V.,
Switching Convolutional Neural Network for Crowd Counting,
CVPR17(4031-4039)
IEEE DOI
1711
Head, Neural networks,
Relays, Switches, Training
BibRef
Shukla, S.[Shivang],
Tiddeman, B.[Bernard],
Miles, H.C.[Helen C.],
A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D
Training Simulator,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Ji, Q.G.[Qing-Ge],
Chen, H.[Hang],
Bao, D.[Di],
Improving crowd counting with scale-aware convolutional neural
network,
IET-IPR(15), No. 10, 2021, pp. 2192-2201.
DOI Link
2108
BibRef
Ding, X.H.[Xing-Hao],
He, F.[Fujin],
Lin, Z.R.[Zhi-Rui],
Wang, Y.[Yu],
Guo, H.M.[Hui-Min],
Huang, Y.[Yue],
Crowd Density Estimation Using Fusion of Multi-Layer Features,
ITS(22), No. 8, August 2021, pp. 4776-4787.
IEEE DOI
2108
Feature extraction, Head, Task analysis, Semantics, Estimation, Kernel,
Intelligent transportation systems, Crowd counting, fusion,
density map
BibRef
Liu, X.Y.[Xin-Yue],
Sang, J.[Jun],
Wu, W.Q.[Wei-Qun],
Liu, K.[Kai],
Liu, Q.[Qi],
Xia, X.F.[Xiao-Feng],
Density-aware and background-aware network for crowd counting via
multi-task learning,
PRL(150), 2021, pp. 221-227.
Elsevier DOI
2109
Crowd counting, Multi-task learning, Density map, Auxiliary task
BibRef
Zhu, A.[Aichun],
Duan, G.X.[Guo-Xiu],
Zhu, X.M.[Xiao-Mei],
Zhao, L.[Lu],
Huang, Y.Y.[Yao-Ying],
Hua, G.[Gang],
Snoussi, H.[Hichem],
CDADNet: Context-guided dense attentional dilated network for crowd
counting,
SP:IC(98), 2021, pp. 116379.
Elsevier DOI
2109
Crowd counting, Density map, Dense dilated, Attention
BibRef
Wang, T.[Tian],
Chen, Y.[Yang],
Lin, Z.W.[Zhi-Wei],
Zhu, A.[Aichun],
Li, Y.[Yong],
Snoussi, H.[Hichem],
Wang, H.[Hui],
RecapNet: Action Proposal Generation Mimicking Human Cognitive
Process,
Cyber(51), No. 12, December 2021, pp. 6017-6028.
IEEE DOI
2112
Proposals, Videos, Convolution, Video sequences,
Cognitive processes, Object recognition, Action detection,
residual causal convolution
BibRef
Chu, H.P.[Huan-Peng],
Tang, J.L.[Ji-Lin],
Hu, H.J.[Hao-Ji],
Attention guided feature pyramid network for crowd counting,
JVCIR(80), 2021, pp. 103319.
Elsevier DOI
2110
Crowd counting, Feature pyramid network, Attention mechanism,
Density map generation
BibRef
Gao, J.Y.[Jun-Yu],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
Feature-Aware Adaptation and Density Alignment for Crowd Counting in
Video Surveillance,
Cyber(51), No. 10, October 2021, pp. 4822-4833.
IEEE DOI
2110
Feature extraction, Training, Task analysis, Adaptation models,
Data models, Labeling, Crowd counting,
unsupervised domain adaptation
BibRef
Kakaletsis, E.[Efstratios],
Mademlis, I.[Ioannis],
Nikolaidis, N.[Nikos],
Pitas, I.[Ioannis],
Multiview vision-based human crowd localization for UAV fleet flight
safety,
SP:IC(99), 2021, pp. 116484.
Elsevier DOI
2111
Crowd detection, Drone vision, Image processing,
Autonomous drones, Multiview fusion
BibRef
Symeonidis, C.[Charalampos],
Mademlis, I.[Ioannis],
Pitas, I.[Ioannis],
Nikolaidis, N.[Nikos],
Auth-Persons: A Dataset for Detecting Humans in Crowds from Aerial
Views,
ICIP22(596-600)
IEEE DOI
2211
Training, Visualization, Pipelines, Neural networks, Detectors, Safety,
Sensors, person detection, Unmanned Aerial Vehicles,
Non-Maximum Suppression
BibRef
Zhou, Y.[Yuan],
Yang, J.X.[Jian-Xing],
Li, H.[Hongru],
Cao, T.[Tao],
Kung, S.Y.[Sun-Yuan],
Adversarial Learning for Multiscale Crowd Counting Under Complex
Scenes,
Cyber(51), No. 11, November 2021, pp. 5423-5432.
IEEE DOI
2112
BibRef
Earlier: A2, A1, A5, Only:
Multi-scale Generative Adversarial Networks for Crowd Counting,
ICPR18(3244-3249)
IEEE DOI
1812
Generators, Feature extraction, Sociology, Statistics, Estimation,
Training, Task analysis, Adversarial learning, crowd counting,
multiscale generator.
Estimation, Convolution, Generative adversarial networks, Training
BibRef
Lei, T.[Tao],
Zhang, D.[Dong],
Wang, R.S.[Ri-Sheng],
Li, S.Y.[Shu-Ying],
Zhang, W.J.[Wei-Jiang],
Nandi, A.K.[Asoke K.],
MFP-Net: Multi-scale feature pyramid network for crowd counting,
IET-IPR(15), No. 14, 2021, pp. 3522-3533.
DOI Link
2112
BibRef
Zeng, X.[Xin],
Guo, Q.[Qiang],
Duan, H.R.[Hao-Ran],
Wu, Y.P.[Yun-Peng],
Multi-level features extraction network with gating mechanism for
crowd counting,
IET-IPR(15), No. 14, 2021, pp. 3534-3542.
DOI Link
2112
BibRef
Zan, C.T.[Chang-Tong],
Liu, B.[Baodi],
Guan, W.L.[Wei-Li],
Zhang, K.[Kai],
Liu, W.F.[Wei-Feng],
Learn from Object Counting: Crowd Counting with Meta-learning,
IET-IPR(15), No. 14, 2021, pp. 3543-3550.
DOI Link
2112
BibRef
Ekanayake, E.M.C.,
Lei, Y.Q.[Yun-Qi],
Crowd estimation using key-point matching with support vector
regression,
IET-IPR(15), No. 14, 2021, pp. 3551-3558.
DOI Link
2112
BibRef
Zhao, H.Y.[Hao-Yu],
Min, W.D.[Wei-Dong],
Wei, X.[Xin],
Wang, Q.[Qi],
Fu, Q.[Qiyan],
Wei, Z.[Zitai],
MSR-FAN: Multi-scale residual feature-aware network for crowd
counting,
IET-IPR(15), No. 14, 2021, pp. 3512-3521.
DOI Link
2112
BibRef
Ma, Y.J.[Yu-Jen],
Shuai, H.H.[Hong-Han],
Cheng, W.H.[Wen-Huang],
Spatiotemporal Dilated Convolution With Uncertain Matching for
Video-Based Crowd Estimation,
MultMed(24), 2022, pp. 261-273.
IEEE DOI
2202
Feature extraction, Convolution, Training,
Spatiotemporal phenomena, Annotations,
spatiotemporal modeling
BibRef
Reddy, M.K.K.[Mahesh Kumar Krishna],
Rochan, M.[Mrigank],
Lu, Y.W.[Yi-Wei],
Wang, Y.[Yang],
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting,
MultMed(24), 2022, pp. 1008-1019.
IEEE DOI
2202
Adaptation models, Cameras, Data models, Computational modeling,
Backpropagation, Training data, Training, crowd counting,
scene adaptation
BibRef
Xu, C.F.[Chen-Feng],
Liang, D.K.[Ding-Kang],
Xu, Y.C.[Yong-Chao],
Bai, S.[Song],
Zhan, W.[Wei],
Bai, X.[Xiang],
Tomizuka, M.[Masayoshi],
AutoScale: Learning to Scale for Crowd Counting,
IJCV(130), No. 2, February 2022, pp. 405-434.
Springer DOI
2202
BibRef
Delussu, R.[Rita],
Putzu, L.[Lorenzo],
Fumera, G.[Giorgio],
Scene-specific crowd counting using synthetic training images,
PR(124), 2022, pp. 108484.
Elsevier DOI
2203
Crowd counting, Scene-specific settings, Synthetic training images
BibRef
Ledda, E.[Emanuele],
Putzu, L.[Lorenzo],
Delussu, R.[Rita],
Loddo, A.[Andrea],
Fumera, G.[Giorgio],
How Realistic Should Synthetic Images Be for Training Crowd Counting
Models?,
CAIP21(II:46-56).
Springer DOI
2112
BibRef
Sindagi, V.A.[Vishwanath A.],
Yasarla, R.[Rajeev],
Patel, V.M.[Vishal M.],
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark
Method,
PAMI(44), No. 5, May 2022, pp. 2594-2609.
IEEE DOI
2204
Annotations, Task analysis, Training, Head, Meteorology,
Benchmark testing, Learning systems, Crowd counting, dataset
BibRef
Yan, Z.Y.[Zhao-Yi],
Zhang, R.[Ruimao],
Zhang, H.Z.[Hong-Zhi],
Zhang, Q.F.[Qing-Fu],
Zuo, W.M.[Wang-Meng],
Crowd Counting Via Perspective-Guided Fractional-Dilation Convolution,
MultMed(24), 2022, pp. 2633-2647.
IEEE DOI
2205
Convolution, Estimation, Feature extraction,
Kernel, Computer science, Annotations, Surveillance, neural network,
supervised learning
BibRef
Yan, Z.Y.[Zhao-Yi],
Yuan, Y.C.[Yu-Chen],
Zuo, W.M.[Wang-Meng],
Tan, X.[Xiao],
Wang, Y.Z.[Ye-Zhen],
Wen, S.L.[Shi-Lei],
Ding, E.R.[Er-Rui],
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
Ptak, B.[Bartosz],
Pieczynski, D.[Dominik],
Piechocki, M.[Mateusz],
Kraft, M.[Marek],
On-Board Crowd Counting and Density Estimation Using Low Altitude
Unmanned Aerial Vehicles: Looking beyond Beating the Benchmark,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Zhou, J.T.Y.[Joey Tian-Yi],
Zhang, L.[Le],
Du, J.W.[Jia-Wei],
Peng, X.[Xi],
Fang, Z.W.[Zhi-Wen],
Xiao, Z.[Zhe],
Zhu, H.Y.[Hong-Yuan],
Locality-Aware Crowd Counting,
PAMI(44), No. 7, July 2022, pp. 3602-3613.
IEEE DOI
2206
Training, Training data, Task analysis,
Feature extraction, Data models, Optimization,
adversarial defense
BibRef
Zhou, L.F.[Li-Fang],
Wang, P.[Peiwen],
Li, W.S.[Wei-Sheng],
Leng, J.X.[Jia-Xu],
Lei, B.J.[Bang-Jun],
Semantic-refined spatial pyramid network for crowd counting,
PRL(159), 2022, pp. 9-15.
Elsevier DOI
2206
Crowd counting, Multi-scale, Semantic-refined, Convolutional neural network
BibRef
Wang, Q.[Qi],
Lin, W.[Wei],
Gao, J.Y.[Jun-Yu],
Li, X.L.[Xue-Long],
Density-Aware Curriculum Learning for Crowd Counting,
Cyber(52), No. 6, June 2022, pp. 4675-4687.
IEEE DOI
2207
Training, Decoding, Feature extraction, Estimation, Standards,
Neural networks, Task analysis, Crowd counting
BibRef
Zhu, A.[Aichun],
Zheng, Z.[Zhe],
Huang, Y.[Yaoying],
Wang, T.[Tian],
Jin, J.[Jing],
Hu, F.Q.[Fang-Qiang],
Hua, G.[Gang],
Snoussi, H.[Hichem],
CACrowdGAN:
Cascaded Attentional Generative Adversarial Network for Crowd Counting,
ITS(23), No. 7, July 2022, pp. 8090-8102.
IEEE DOI
2207
Generators, Generative adversarial networks,
Computational modeling, Training, Head, Task analysis, Kernel,
attention mechanism
BibRef
Zhang, Q.[Qi],
Chan, A.B.[Antoni B.],
Wide-Area Crowd Counting: Multi-view Fusion Networks for Counting in
Large Scenes,
IJCV(130), No. 8, August 2022, pp. 1938-1960.
Springer DOI
2207
BibRef
Earlier:
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View
Fusion CNNs,
CVPR19(8289-8298).
IEEE DOI
2002
BibRef
Zhao, W.[Wenda],
Wang, M.Y.[Ming-Yue],
Liu, Y.[Yu],
Lu, H.M.[Hui-Min],
Xu, C.[Congan],
Yao, L.[Libo],
Generalizable Crowd Counting via Diverse Context Style Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5399-5410.
IEEE DOI
2208
Training, Logic gates, Redundancy, Lighting, Degradation,
Mean square error methods, Visualization, generalized crowd counting
BibRef
Zhao-Xin, L.[Li],
Shuhua, L.[Lu],
Ling-Qiang, L.[Lan],
Qi-Yuan, L.[Liu],
Crowd counting in complex scenes based on an attention aware CNN
network,
JVCIR(87), 2022, pp. 103591.
Elsevier DOI
2208
Crowd counting, Density estimation, Attentive maps
BibRef
Huo, J.B.[Jin-Biao],
Fu, X.[Xiao],
Liu, Z.Y.[Zhi-Yuan],
Zhang, Q.[Qi],
Short-Term Estimation and Prediction of Pedestrian Density in Urban
Hot Spots Based on Mobile Phone Data,
ITS(23), No. 8, August 2022, pp. 10827-10838.
IEEE DOI
2208
Mobile handsets, Estimation, Kalman filters, Rail transportation,
Predictive models, Forecasting, Pedestrian density estimation,
mobile phone data
BibRef
Zhong, X.[Xin],
Yan, Z.Y.[Zhao-Yi],
Qin, J.[Jing],
Zuo, W.M.[Wang-Meng],
Lu, W.G.[Wei-Gang],
An Improved Normed-Deformable Convolution for Crowd Counting,
SPLetters(29), 2022, pp. 1794-1798.
IEEE DOI
2209
Convolution, Head, Training, Visualization, Oceans, Measurement, Kernel,
Crowd counting, normed-deformable convolution, uniform sampling
BibRef
Wang, Q.[Qian],
Breckon, T.P.[Toby P.],
Crowd Counting via Segmentation Guided Attention Networks and
Curriculum Loss,
ITS(23), No. 9, September 2022, pp. 15233-15243.
IEEE DOI
2209
Training, Convolutional neural networks, Image segmentation,
Estimation, Neural networks, Kernel, Task analysis, Crowd counting,
segmentation guided attention networks
BibRef
Liu, W.Z.[Wei-Zhe],
Salzmann, M.[Mathieu],
Fua, P.[Pascal],
Counting People by Estimating People Flows,
PAMI(44), No. 11, November 2022, pp. 8151-8166.
IEEE DOI
2210
BibRef
Earlier:
Estimating People Flows to Better Count Them in Crowded Scenes,
ECCV20(XV:723-740).
Springer DOI
2011
Training, Video sequences, Feature extraction, Pattern analysis,
Optical imaging, Annotations, Crowd counting, temporal consistency, surveillance
BibRef
Mo, H.[Hong],
Ren, W.Q.[Wen-Qi],
Zhang, X.[Xiong],
Yan, F.H.[Fei-Hu],
Zhou, Z.[Zhong],
Cao, X.C.[Xiao-Chun],
Wu, W.[Wei],
Attention-Guided Collaborative Counting,
IP(31), 2022, pp. 6306-6319.
IEEE DOI
2210
Feature extraction, Collaboration, Transformers, Task analysis, Head,
Computational modeling, Crowd counting,
bi-directional transformer
BibRef
Liu, Y.B.[Yan-Bo],
Cao, G.[Guo],
Shi, H.[Hao],
Hu, Y.X.[Ying-Xiang],
Lw-Count: An Effective Lightweight Encoding-Decoding Crowd Counting
Network,
CirSysVideo(32), No. 10, October 2022, pp. 6821-6834.
IEEE DOI
2210
Feature extraction, Data mining, Correlation,
Graphics processing units, Decoding, Costs, Computer science,
regional normalized cross-correlation loss
BibRef
Zhang, A.[Anran],
Xu, J.[Jun],
Luo, X.Y.[Xiao-Yan],
Cao, X.B.[Xian-Bin],
Zhen, X.T.[Xian-Tong],
Cross-Domain Attention Network for Unsupervised Domain Adaptation
Crowd Counting,
CirSysVideo(32), No. 10, October 2022, pp. 6686-6699.
IEEE DOI
2210
Feature extraction, Task analysis, Adversarial machine learning,
Adaptation models, Decoding, Training, Labeling, Crowd counting,
unsupervised learning
BibRef
Zhang, A.[Anran],
Yang, Y.D.[Yan-Dan],
Xu, J.[Jun],
Cao, X.B.[Xian-Bin],
Zhen, X.T.[Xian-Tong],
Shao, L.[Ling],
Latent Domain Generation for Unsupervised Domain Adaptation Object
Counting,
MultMed(25), 2023, pp. 1773-1783.
IEEE DOI
2306
Generators, Adaptation models, Stochastic processes, Training,
Task analysis, Perturbation methods, Labeling, Object counting,
unsupervised learning
BibRef
Zhang, Q.[Qi],
Chan, A.B.[Antoni B.],
3D Crowd Counting via Geometric Attention-Guided Multi-view Fusion,
IJCV(130), No. 12, December 2022, pp. 3123-3139.
Springer DOI
2211
BibRef
Zhang, Q.[Qi],
Lin, W.[Wei],
Chan, A.B.[Antoni B.],
Cross-View Cross-Scene Multi-View Crowd Counting,
CVPR21(557-567)
IEEE DOI
2111
Training, Geometry, Adaptation models, Fuses, Layout, Cameras, Data models
BibRef
Wan, J.[Jia],
Liu, Z.Q.[Zi-Quan],
Chan, A.B.[Antoni B.],
A Generalized Loss Function for Crowd Counting and Localization,
CVPR21(1974-1983)
IEEE DOI
2111
Location awareness, Annotations,
Cost function, Bayes methods, Pattern recognition
BibRef
Lian, D.Z.[Dong-Ze],
Chen, X.N.[Xia-Ning],
Li, J.[Jing],
Luo, W.X.[Wei-Xin],
Gao, S.H.[Sheng-Hua],
Locating and Counting Heads in Crowds With a Depth Prior,
PAMI(44), No. 12, December 2022, pp. 9056-9072.
IEEE DOI
2212
Head, Magnetic heads, Location awareness, Feature extraction, Games,
Object detection, Annotations, Crowd counting, head localization, RGB-D
BibRef
Xu, Y.Y.[Yan-Yu],
Zhong, Z.M.[Zi-Ming],
Lian, D.Z.[Dong-Ze],
Li, J.[Jing],
Li, Z.X.[Zheng-Xin],
Xu, X.X.[Xin-Xing],
Gao, S.H.[Sheng-Hua],
Crowd Counting With Partial Annotations in an Image,
ICCV21(15550-15559)
IEEE DOI
2203
Visualization, Costs, Head, Codes, Annotations, Computational modeling,
Scene analysis and understanding,
BibRef
Zhou, W.[Wujie],
Pan, Y.[Yi],
Lei, J.S.[Jing-Sheng],
Ye, L.[Lv],
Yu, L.[Lu],
DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd
Counting,
ITS(23), No. 12, December 2022, pp. 24540-24549.
IEEE DOI
2212
Feature extraction, Lighting, Data mining, Optical imaging, Fuses,
Cameras, Task analysis, RGB-T image, dual-branch, deep learning
BibRef
Wu, Z.[Zhe],
Zhang, X.F.[Xin-Feng],
Tian, G.[Geng],
Wang, Y.[Yaowei],
Huang, Q.M.[Qing-Ming],
Spatial-Temporal Graph Network for Video Crowd Counting,
CirSysVideo(33), No. 1, January 2023, pp. 228-241.
IEEE DOI
2301
Computational modeling, Predictive models, Analytical models,
Long short term memory, Optical flow,
multi-scale module
BibRef
Khan, M.A.[Muhammad Asif],
Menouar, H.[Hamid],
Hamila, R.[Ridha],
Revisiting crowd counting: State-of-the-art, trends, and future
perspectives,
IVC(129), 2023, pp. 104597.
Elsevier DOI
2301
Crowd counting, CNN, Density estimation, Evaluation metrics,
Loss functions, Transformers
BibRef
Zhang, S.[Shihui],
Wang, W.[Wei],
Zhao, W.[Weibo],
Wang, L.[Lei],
Li, Q.[Qunpeng],
A cross-modal crowd counting method combining CNN and cross-modal
transformer,
IVC(129), 2023, pp. 104592.
Elsevier DOI
2301
Cross-modal crowd counting, CNN, Transformer,
Cross layer connection structure, Cross-modal attention module
BibRef
Wang, L.[Lin],
Li, J.[Jie],
Zhang, S.Q.[Si-Qi],
Qi, C.[Chun],
Wang, P.[Pan],
Wang, F.[Fengping],
Multi-Scale and spatial position-based channel attention network for
crowd counting,
JVCIR(90), 2023, pp. 103718.
Elsevier DOI
2301
Crowd counting, Spatial position-based channel attention model, Adaptive loss
BibRef
Liu, Z.Y.[Zheng-Yi],
Tan, Y.C.[Ya-Cheng],
Wu, W.[Wei],
Tang, B.[Bin],
Dilated high-resolution network driven RGB-T multi-modal crowd
counting,
SP:IC(112), 2023, pp. 116915.
Elsevier DOI
2302
Crowd counting, RGB-T image, Multi-modal, High-resolution, Multilayer perceptron
BibRef
Tang, W.X.[Wen-Xiao],
Liu, K.[Kun],
Shakeel, M.S.[M. Saad],
Wang, H.[Hao],
Kang, W.X.[Wen-Xiong],
DDAD: Detachable Crowd Density Estimation Assisted Pedestrian
Detection,
ITS(24), No. 2, February 2023, pp. 1867-1878.
IEEE DOI
2302
Estimation, Annotations, Task analysis, Feature extraction, Head,
Detectors, Multitasking, Pedestrian detection, multi-task learning,
crowd density estimation
BibRef
Zhang, X.G.[Xing-Guo],
Sun, Y.P.[Yin-Ping],
Li, Q.Z.[Qi-Ze],
Li, X.D.[Xiao-Di],
Shi, X.Y.[Xin-Yu],
Crowd Density Estimation and Mapping Method Based on Surveillance
Video and GIS,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Gu, S.Q.[Si-Qi],
Lian, Z.C.[Zhi-Chao],
A unified RGB-T crowd counting learning framework,
IVC(131), 2023, pp. 104631.
Elsevier DOI
2303
Crowd counting, RGB-T, Multimodal fusion, End-to-end training
BibRef
Chen, Y.[Yuehai],
Yang, J.[Jing],
Chen, B.[Badong],
Du, S.[Shaoyi],
Counting Varying Density Crowds Through Density Guided Adaptive
Selection CNN and Transformer Estimation,
CirSysVideo(33), No. 3, March 2023, pp. 1055-1068.
IEEE DOI
2303
Transformers, Estimation, Convolutional neural networks, Kernel,
Annotations, Location awareness, Task analysis, Crowd counting,
adaptive selection
BibRef
Chen, J.[Jiwei],
Wang, Z.F.[Zeng-Fu],
Multi-task semi-supervised crowd counting via global to local
self-correction,
PR(140), 2023, pp. 109506.
Elsevier DOI
2305
Crowd counting, Semi-supervised, Pseudo labels, Global to local self-correction
BibRef
Zhao, H.Y.[Hao-Yu],
Wang, Q.[Qi],
Zhan, G.[Guowei],
Min, W.D.[Wei-Dong],
Zou, Y.[Yi],
Cui, S.M.[Shi-Miao],
Need Only One More Point (NOOMP): Perspective Adaptation Crowd
Counting in Complex Scenes,
MultMed(25), 2023, pp. 1414-1426.
IEEE DOI
2305
Adaptation models, Training, Task analysis, Labeling,
Computer science, Kernel, Feature extraction, Crowd counting,
perspective-adaptive
BibRef
Wang, R.[Rui],
Hao, Y.X.[Yi-Xue],
Hu, L.[Long],
Chen, J.[Jincai],
Chen, M.[Min],
Wu, D.[Di],
Self-Supervised Learning With Data-Efficient Supervised Fine-Tuning
for Crowd Counting,
MultMed(25), 2023, pp. 1538-1546.
IEEE DOI
2305
Data models, Head, Self-supervised learning, Annotations, Training,
Task analysis, Computational modeling, Crowd counting, self-supervised loss
BibRef
Ma, J.J.[Jun-Jie],
Dai, Y.P.[Ya-Ping],
Jia, Z.Y.[Zhi-Yang],
Sun, F.C.[Fu-Chun],
Tan, Y.P.[Yap-Peng],
Liu, J.[Jun],
Crowd counting from single images using recursive multi-pathway
zooming and foreground enhancement,
PR(141), 2023, pp. 109585.
Elsevier DOI
2306
Crowd counting, Density estimation, Multi-Pathway zooming,
Foreground enhancement
BibRef
Wang, M.J.[Ming-Jie],
Cai, H.[Hao],
Han, X.F.[Xian-Feng],
Zhou, J.[Jun],
Gong, M.L.[Ming-Lun],
STNet: Scale Tree Network With Multi-Level Auxiliator for Crowd
Counting,
MultMed(25), 2023, pp. 2074-2084.
IEEE DOI
2306
Task analysis, Training, Estimation, Feature extraction, Semantics,
Computer science, Cognition, Tree structure, scale enhancer,
crowd counting
BibRef
Savner, S.S.[Siddharth Singh],
Kanhangad, V.[Vivek],
CrowdFormer: Weakly-supervised crowd counting with improved
generalizability,
JVCIR(94), 2023, pp. 103853.
Elsevier DOI
2306
Crowd counting, Vision transformers, Weakly-supervised method, Generalizability
BibRef
Li, S.L.[Sheng-Lei],
Hishiyama, R.[Reiko],
Counting and Tracking People to Avoid from Crowded in a Restaurant
Using mmWave Radar,
IEICE(E106-D), No. 6, June 2023, pp. 1142-1154.
WWW Link.
2306
BibRef
Liu, Y.T.[Yong-Tuo],
Ren, S.C.[Su-Cheng],
Chai, L.Y.[Liang-Yu],
Wu, H.J.[Han-Jie],
Xu, D.[Dan],
Qin, J.[Jing],
He, S.F.[Sheng-Feng],
Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd
Counting,
PAMI(45), No. 7, July 2023, pp. 9248-9255.
IEEE DOI
2306
Labeling, Redundancy, Training, Feature extraction,
Termination of employment, Head, Technological innovation,
spatial labeling redundancy
BibRef
Du, Z.P.[Zhi-Peng],
Shi, M.J.[Miao-Jing],
Deng, J.K.[Jian-Kang],
Zafeiriou, S.P.[Stefanos P.],
Redesigning Multi-Scale Neural Network for Crowd Counting,
IP(32), 2023, pp. 3664-3678.
IEEE DOI
2307
Estimation, Neural networks, Task analysis, Feature extraction,
Computer architecture, Optimization, Deep learning, Crowd counting,
relative local counting
BibRef
Wang, X.[Xin],
Zhan, Y.[Yue],
Zhao, Y.[Yang],
Yang, T.[Tangwen],
Ruan, Q.Q.[Qiu-Qi],
Semi-Supervised Crowd Counting With Spatial Temporal Consistency and
Pseudo-Label Filter,
CirSysVideo(33), No. 8, August 2023, pp. 4190-4203.
IEEE DOI
2308
Training, Feature extraction, Task analysis, Perturbation methods,
Uncertainty, Reliability, Predictive models,
pseudo-label filter
BibRef
Bai, H.Y.[Hao-Yue],
He, H.[Hao],
Peng, Z.X.[Zhuo-Xuan],
Dai, T.Y.[Tian-Yuan],
Chan, S.-.H.G.[S.-H. Gary],
Countr: An End-to-end Transformer Approach for Crowd Counting and
Density Estimation,
DSC22(207-222).
Springer DOI
2304
BibRef
Dosi, M.[Muskan],
Thakral, K.[Kartik],
Mittal, S.[Surbhi],
Vatsa, M.[Mayank],
Singh, R.[Richa],
AECNet: Attentive EfficientNet For Crowd Counting,
FG4COVID19-21(1-8)
IEEE DOI
2303
Pandemics, Face recognition, Pipelines, Neural networks, Lighting,
Gesture recognition, Benchmark testing
BibRef
Wang, M.J.[Ming-Jie],
Cai, H.[Hao],
Dai, Y.[Yong],
Gong, M.L.[Ming-Lun],
Dynamic Mixture of Counter Network for Location-Agnostic Crowd
Counting,
WACV23(167-177)
IEEE DOI
2302
Protocols, Annotations, Focusing, Benchmark testing, Transformers, Mixers
BibRef
Zhang, Q.[Qi],
Chan, A.B.[Antoni B.],
Calibration-Free Multi-view Crowd Counting,
ECCV22(IX:227-244).
Springer DOI
2211
BibRef
Sam, D.B.[Deepak Babu],
Agarwalla, A.[Abhinav],
Joseph, J.[Jimmy],
Sindagi, V.A.[Vishwanath A.],
Babu, R.V.[R. Venkatesh],
Patel, V.M.[Vishal M.],
Completely Self-supervised Crowd Counting via Distribution Matching,
ECCV22(XXXI:186-204).
Springer DOI
2211
BibRef
Ma, Y.M.[Yi-Ming],
Sanchez, V.[Victor],
Guha, T.[Tanaya],
Fusioncount: Efficient Crowd Counting Via Multiscale Feature Fusion,
ICIP22(3256-3260)
IEEE DOI
2211
Image coding, Databases, Computational modeling,
Feature extraction, Distortion, Encoding, Crowd density estimation,
efficient crowd counting
BibRef
Nguyen, P.[Pha],
Truong, T.D.[Thanh-Dat],
Huang, M.Q.[Miao-Qing],
Liang, Y.[Yi],
Le, N.[Ngan],
Luu, K.[Khoa],
Self-Supervised Domain Adaptation in Crowd Counting,
ICIP22(2786-2790)
IEEE DOI
2211
Training, Learning systems, Error analysis, Lighting, Estimation,
Manuals, Crowd Counting, Domain Adaptation, Entropy Minimization,
Adversarial Learning
BibRef
Pahwa, E.[Esha],
Kapadia, S.[Sanjeet],
Luthra, A.[Achleshwar],
Sheeranali, S.[Shreyas],
Conditional RGB-T Fusion for Effective Crowd Counting,
ICIP22(376-380)
IEEE DOI
2211
Training, Lighting, Focusing, Human factors, Gray-scale,
Video surveillance, Distortion, Crowd Counting, Multi-Modal Fusion,
Thermal Imagery
BibRef
Shu, W.[Weibo],
Wan, J.[Jia],
Tan, K.C.[Kay Chen],
Kwong, S.[Sam],
Chan, A.B.[Antoni B.],
Crowd Counting in the Frequency Domain,
CVPR22(19586-19595)
IEEE DOI
2210
Training, Upper bound, Tensors, Image analysis,
Frequency-domain analysis, Machine vision, Design methodology,
Vision applications and systems
BibRef
Lin, H.[Hui],
Ma, Z.H.[Zhi-Heng],
Ji, R.R.[Rong-Rong],
Wang, Y.[Yaowei],
Hong, X.P.[Xiao-Peng],
Boosting Crowd Counting via Multifaceted Attention,
CVPR22(19596-19605)
IEEE DOI
2210
Training, Convolution, Computational modeling, Pipelines,
Transformers, Encoding, Scene analysis and understanding,
Representation learning
BibRef
Liu, W.Z.[Wei-Zhe],
Durasov, N.[Nikita],
Fua, P.[Pascal],
Leveraging Self-Supervision for Cross-Domain Crowd Counting,
CVPR22(5331-5342)
IEEE DOI
2210
Training, Uncertainty, Image recognition, Annotations, Force,
Prediction algorithms, Recognition: detection, categorization,
Scene analysis and understanding
BibRef
Gong, S.[Shenjian],
Zhang, S.S.[Shan-Shan],
Yang, J.[Jian],
Dai, D.X.[Deng-Xin],
Schiele, B.[Bernt],
Bi-level Alignment for Cross-Domain Crowd Counting,
CVPR22(7532-7540)
IEEE DOI
2210
Training, Annotations, Estimation, Training data, Transforms, Manuals,
Recognition: detection, categorization, retrieval,
Transfer/low-shot/long-tail learning
BibRef
Ledda, E.[Emanuele],
Putzu, L.[Lorenzo],
Delussu, R.[Rita],
Fumera, G.[Giorgio],
Roli, F.[Fabio],
On the Evaluation of Video-Based Crowd Counting Models,
CIAP22(III:301-311).
Springer DOI
2205
BibRef
Ma, Z.H.[Zhi-Heng],
Hong, X.P.[Xiao-Peng],
Wei, X.[Xing],
Qiu, Y.F.[Yun-Feng],
Gong, Y.H.[Yi-Hong],
Towards A Universal Model for Cross-Dataset Crowd Counting,
ICCV21(3185-3194)
IEEE DOI
2203
Sensitivity, Closed-form solutions, Image resolution,
Neural networks, Layout, Estimation,
Scene analysis and understanding
BibRef
Meng, Y.[Yanda],
Zhang, H.R.[Hong-Run],
Zhao, Y.T.[Yi-Tian],
Yang, X.Y.[Xiao-Yun],
Qian, X.S.[Xue-Sheng],
Huang, X.W.[Xiao-Wei],
Zheng, Y.L.[Ya-Lin],
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting,
ICCV21(15529-15539)
IEEE DOI
2203
Representation learning, Image segmentation, Uncertainty,
Annotations, Perturbation methods, Predictive models,
BibRef
Chen, B.H.[Bing-Hui],
Yan, Z.Y.[Zhao-Yi],
Li, K.[Ke],
Li, P.Y.[Peng-Yu],
Wang, B.[Biao],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
Variational Attention: Propagating Domain-Specific Knowledge for
Multi-Domain Learning in Crowd Counting,
ICCV21(16045-16055)
IEEE DOI
2203
Training, Knowledge engineering, Benchmark testing, Data models,
Labeling, Scene analysis and understanding,
Vision applications and systems
BibRef
Song, Q.Y.[Qing-Yu],
Wang, C.[Changan],
Jiang, Z.K.[Zheng-Kai],
Wang, Y.[Yabiao],
Tai, Y.[Ying],
Wang, C.J.[Cheng-Jie],
Li, J.L.[Ji-Lin],
Huang, F.Y.[Fei-Yue],
Wu, Y.[Yang],
Rethinking Counting and Localization in Crowds:
A Purely Point-Based Framework,
ICCV21(3345-3354)
IEEE DOI
2203
Location awareness, Performance evaluation, Head, Codes, Annotations,
Benchmark testing, Detection and localization in 2D and 3D,
BibRef
Wang, C.[Changan],
Song, Q.Y.[Qing-Yu],
Zhang, B.[Boshen],
Wang, Y.[Yabiao],
Tai, Y.[Ying],
Hu, X.[Xuyi],
Wang, C.J.[Cheng-Jie],
Li, J.L.[Ji-Lin],
Ma, J.Y.[Jia-Yi],
Wu, Y.[Yang],
Uniformity in Heterogeneity: Diving Deep into Count Interval
Partition for Crowd Counting,
ICCV21(3214-3222)
IEEE DOI
2203
Quantization (signal), Costs, Codes, Task analysis,
Detection and localization in 2D and 3D,
BibRef
Liu, X.Y.[Xin-Yan],
Li, G.R.[Guo-Rong],
Han, Z.J.[Zhen-Jun],
Zhang, W.G.[Wei-Gang],
Yang, Y.F.[Yi-Fan],
Huang, Q.M.[Qing-Ming],
Sebe, N.[Nicu],
Exploiting sample correlation for crowd counting with multi-expert
network,
ICCV21(3195-3204)
IEEE DOI
2203
Training, Measurement, Deconvolution, Correlation,
Design methodology, Training data,
Efficient training and inference methods
BibRef
Yang, J.H.[Jin-Hai],
Yang, H.[Hua],
MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep
Crowd Segmentation In Video Scenes,
ICIP21(2294-2298)
IEEE DOI
2201
Integrated optics, Deep learning, Image segmentation,
Image motion analysis, Annotations, Motion segmentation, Semantics,
unsupervised semantic segmentation
BibRef
Murayama, K.[Kazuki],
Kanai, K.[Kenji],
Takeuchi, M.[Masaru],
Sun, H.M.[He-Ming],
Katto, J.[Jiro],
Deep Pedestrian Density Estimation for Smart City Monitoring,
ICIP21(230-234)
IEEE DOI
2201
Visualization, Smart cities, Estimation, Transportation, Radar,
Radar imaging, Maintenance engineering, density estimation,
mobile sensing
BibRef
Ding, L.H.[Lai-Hui],
Wang, S.K.[Sheng-Ke],
Li, R.[Rui],
Chen, L.[Long],
Dong, J.Y.[Jun-Yu],
PC-PINet: Partial Re-identification Network for People Counting with
Overlapping Cameras,
ICIVC21(66-71)
IEEE DOI
2112
Detectors, Cameras, Video surveillance, Feature extraction,
Convolutional neural networks, Reliability, Task analysis,
partial re-identification network (PINet)
BibRef
Wang, Y.T.[Yu-Tong],
Li, G.[Gen],
Zhang, Q.[Qi],
Kim, J.[Joongkyu],
Li, H.F.[Hui-Fang],
Perspective-Aware Density Regression for Crowd Counting,
ICIP21(1214-1218)
IEEE DOI
2201
Image resolution, Estimation, Benchmark testing, Distortion,
Crowd counting, Crowd estimation, Density regression,
Perspective-density interaction
BibRef
Jiang, M.Y.[Min-Yang],
Lin, J.Z.[Jian-Zhe],
Wang, Z.J.[Z. Jane],
ShuffleCount: Task-Specific Knowledge Distillation for Crowd Counting,
ICIP21(999-1003)
IEEE DOI
2201
Knowledge engineering, Training, Performance evaluation,
Computational modeling, Image processing, Benchmark testing, Regulation
BibRef
Sajid, U.[Usman],
Chen, X.Y.[Xiang-Yu],
Sajid, H.[Hasan],
Kim, T.[Taejoon],
Wang, G.H.[Guang-Hui],
Audio-Visual Transformer Based Crowd Counting,
DeepMTL21(2249-2259)
IEEE DOI
2112
Visualization, Computational modeling, Estimation, Benchmark testing
BibRef
Almalki, K.J.[Khalid J],
Choi, B.Y.[Baek-Young],
Chen, Y.[Yu],
Song, S.[Sejun],
Characterizing Scattered Occlusions for Effective Dense-Mode Crowd
Counting,
OVIS21(3833-3842)
IEEE DOI
2112
Heating systems, Deep learning, Convolution,
Mean square error methods, Computer architecture
BibRef
Liu, L.B.[Ling-Bo],
Chen, J.Q.[Jia-Qi],
Wu, H.F.[He-Feng],
Li, G.B.[Guan-Bin],
Li, C.L.[Cheng-Long],
Lin, L.[Liang],
Cross-Modal Collaborative Representation Learning and a Large-Scale
RGBT Benchmark for Crowd Counting,
CVPR21(4821-4831)
IEEE DOI
2111
Codes, Collaboration, Benchmark testing,
Optical imaging, Pattern recognition, Task analysis
BibRef
Wen, L.Y.[Long-Yin],
Du, D.W.[Da-Wei],
Zhu, P.F.[Peng-Fei],
Hu, Q.H.[Qing-Hua],
Wang, Q.L.[Qi-Long],
Bo, L.F.[Lie-Feng],
Lyu, S.W.[Si-Wei],
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark,
CVPR21(7808-7817)
IEEE DOI
2111
Location awareness, Training, Target tracking, Estimation,
Object detection, Benchmark testing, Trajectory
BibRef
Golda, T.[Thomas],
Krüger, F.[Florian],
Beyerer, J.[Jürgen],
Temporal Extension for Encoder-Decoder-based Crowd Counting
Approaches,
MVA21(1-5)
DOI Link
2109
Video sequences, Estimation, Feature extraction,
Time measurement, Safety, Decoding
BibRef
Rong, L.Z.[Liang-Zi],
Li, C.P.[Chun-Ping],
Coarse- and Fine-grained Attention Network with Background-aware Loss
for Crowd Density Map Estimation,
WACV21(3674-3683)
IEEE DOI
2106
Backpropagation, Image quality, Image recognition,
Estimation
BibRef
Modolo, D.[Davide],
Shuai, B.[Bing],
Varior, R.R.[Rahul Rama],
Tighe, J.[Joseph],
Understanding the impact of mistakes on background regions in crowd
counting,
WACV21(1649-1658)
IEEE DOI
2106
Measurement, Education, Standards
BibRef
Zhang, Y.[Yani],
Zhao, H.L.[Huai-Lin],
Zhou, F.B.[Fang-Bo],
Zhang, Q.[Qing],
Shi, Y.J.[Yan-Jiao],
Liang, L.J.[Lan-Jun],
Mscanet: Adaptive Multi-scale Context Aggregation Network for Congested
Crowd Counting,
MMMod21(II:1-12).
Springer DOI
2106
BibRef
Li, L.[Lei],
Dong, Y.[Yuan],
Bai, H.L.[Hong-Liang],
Spatial-related and Scale-aware Network for Crowd Counting,
ICPR21(1-7)
IEEE DOI
2105
Heating systems, Visualization, Convolution, Interference,
Benchmark testing, Pattern recognition, Convolutional neural networks
BibRef
Li, W.X.[Wen-Xi],
Cao, Z.Q.[Zhuo-Qun],
Wang, Q.[Qian],
Chen, S.J.[Song-Jian],
Feng, R.[Rui],
Learning Error-Driven Curriculum for Crowd Counting,
ICPR21(843-849)
IEEE DOI
2105
Training, Benchmark testing, Pattern recognition
BibRef
Guo, D.[Dewen],
Feng, J.[Jie],
Zhou, B.F.[Bing-Feng],
VGG-Embedded Adaptive Layer-Normalized Crowd Counting Net with
Scale-Shuffling Modules,
ICPR21(1475-1482)
IEEE DOI
2105
Training, Image quality, Lighting, Benchmark testing,
Real-time systems, Pattern recognition, Security
BibRef
Meng, S.Q.[Shi-Qiao],
Li, J.J.[Jia-Jie],
Guo, W.W.[Wei-Wei],
Ye, L.[Lai],
Jiang, J.F.[Jin-Feng],
PHNet: Parasite-Host Network for Video Crowd Counting,
ICPR21(1956-1963)
IEEE DOI
2105
Training data, Transforms, Predictive models, Feature extraction,
Robustness, Spatiotemporal phenomena, Image sequences
BibRef
Thanasutives, P.[Pongpisit],
Fukui, K.I.[Ken-Ichi],
Numao, M.[Masayuki],
Kijsirikul, B.[Boonserm],
Encoder-Decoder Based Convolutional Neural Networks with
Multi-Scale-Aware Modules for Crowd Counting,
ICPR21(2382-2389)
IEEE DOI
2105
Training, Adaptation models, Image segmentation, Surveillance,
Neural networks, Semantics, Computer architecture
BibRef
Sajid, U.[Usman],
Ma, W.[Wenchi],
Wang, G.H.[Guang-Hui],
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd
Counting,
ICPR21(5790-5797)
IEEE DOI
2105
Measurement, Head, Fuses, Lighting, Estimation, Benchmark testing,
Pattern recognition, Crowd counting, crowd-density,
input priors
BibRef
Su, X.X.[Xin-Xing],
Yuan, Y.C.[Yu-Chen],
Su, X.B.[Xiang-Bo],
Zou, Z.K.[Zhi-Kang],
Wen, S.L.[Shi-Lei],
Zhou, P.[Pan],
HANet: Hybrid Attention-aware Network for Crowd Counting,
ICPR21(7707-7714)
IEEE DOI
2105
Estimation error, Adaptive systems,
Benchmark testing, Feature extraction, Encoding, Decoding
BibRef
Peng, T.[Tao],
Li, R.[Rong],
Li, S.[Shang],
Zhu, P.F.[Peng-Fei],
Learning from Web Data:
Improving Crowd Counting via Semi-Supervised Learning,
ICPR21(7937-7944)
IEEE DOI
2105
Visualization, Annotations, Neural networks,
Semisupervised learning, Data models, Pattern recognition, Task analysis
BibRef
Peng, T.[Tao],
Li, Q.[Qing],
Zhu, P.F.[Peng-Fei],
RGB-T Crowd Counting from Drone: A Benchmark and Mmccn Network,
ACCV20(VI:497-513).
Springer DOI
2103
BibRef
Ranjan, V.[Viresh],
Wang, B.[Boyu],
Shah, M.[Mubarak],
Hoai, M.[Minh],
Uncertainty Estimation and Sample Selection for Crowd Counting,
ACCV20(V:375-391).
Springer DOI
2103
BibRef
Hossain, M.A.[Mohammad Asiful],
Cannons, K.[Kevin],
Jang, D.[Daesik],
Cuzzolin, F.[Fabio],
Xu, Z.[Zhan],
Video-based Crowd Counting Using a Multi-scale Optical Flow Pyramid
Network,
ACCV20(V:3-20).
Springer DOI
2103
BibRef
Xu, J.,
Yu, L.,
Zhang, J.,
Wu, Q.,
Automatic Sheep Counting by Multi-object Tracking,
VCIP20(257-257)
IEEE DOI
2102
Agriculture, Trajectory, Animals, Video sequences, Transportation,
Task analysis, Cameras
BibRef
Zhou, Z.,
Su, L.,
Li, G.,
Yang, Y.,
Huang, Q.,
CSCNet: A Shallow Single Column Network for Crowd Counting,
VCIP20(535-538)
IEEE DOI
2102
Convolution, Kernel, Switches, Feature extraction, Training,
Crowd Counting,
Receptive Field
BibRef
Liu, X.Y.[Xi-Yang],
Yang, J.[Jie],
Ding, W.R.[Wen-Rui],
Wang, T.Q.[Tie-Qiang],
Wang, Z.J.[Zhi-Jin],
Xiong, J.J.[Jun-Jun],
Adaptive Mixture Regression Network with Local Counting Map for Crowd
Counting,
ECCV20(XXIV:241-257).
Springer DOI
2012
BibRef
Liu, Y.[Yan],
Liu, L.Q.[Ling-Qiao],
Wang, P.[Peng],
Zhang, P.P.[Ping-Ping],
Lei, Y.J.[Yin-Jie],
Semi-Supervised Crowd Counting via Self-training on Surrogate Tasks,
ECCV20(XV:242-259).
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,
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],
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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,
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
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,
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
feature extraction, image representation,
object detection, random processes, nonlocal attention mechanism,
Machine learning
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
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.H.[Wen-Han],
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.Z.[Wei-Zhe],
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.T.[Yu-Ting],
Shi, M.J.[Miao-Jing],
Zhao, Q.J.[Qi-Jun],
Wang, X.F.[Xiao-Fang],
Point in, Box Out: Beyond Counting Persons in Crowds,
CVPR19(6462-6471).
IEEE DOI
2002
BibRef
Shi, M.J.[Miao-Jing],
Yang, Z.H.[Zhao-Hui],
Xu, C.[Chao],
Chen, Q.J.[Qi-Jun],
Revisiting Perspective Information for Efficient Crowd Counting,
CVPR19(7271-7280).
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
object detection, pedestrians,
traffic engineering computing, Scale Pyramid Module
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,
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, 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,
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, Visualization, Estimation,
Head, Context modeling
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, 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
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
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
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
Wang, T.[Tao],
Li, G.H.[Guo-Hui],
Lei, J.[Jun],
Li, S.H.[Shuo-Hao],
Xu, S.K.[Shu-Kui],
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
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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],
Dugelay, J.L.[Jean-Luc],
A new multiclass SVM algorithm and its application to crowd density
analysis using LBP features,
ICIP13(4554-4558)
IEEE DOI
1402
Crowd density
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Loy, C.C.[Chen Change],
Gong, S.G.[Shao-Gang],
Xiang, T.[Tao],
From Semi-supervised to Transfer Counting of Crowds,
ICCV13(2256-2263)
IEEE DOI
1403
crowd counting
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Chen, K.[Ke],
Loy, C.C.[Chen Change],
Gong, S.G.[Shao-Gang],
Xiang, T.[Tony],
Feature Mining for Localised Crowd Counting,
BMVC12(21).
DOI Link
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Fu, H.Y.[Hui-Yuan],
Ma, H.D.[Hua-Dong],
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Real-time accurate crowd counting based on RGB-D information,
ICIP12(2685-2688).
IEEE DOI
1302
BibRef
Yogameena, B.,
Perumal, S.S.[S. Saravana],
Packiyaraj, N.,
Saravanan, P.,
Ma-Th algorithm for people count in a dense crowd and their behaviour
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IMVIP12(17-20).
IEEE DOI
1302
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Scoleri, T.,
Henneberg, M.,
View-Independent Prediction of Body Dimensions in Crowded Environments,
DICTA12(1-8).
IEEE DOI
1303
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Kim, D.[Daehum],
Lee, Y.H.[Young-Hyun],
Ku, B.H.[Bon-Hwa],
Ko, H.S.[Han-Seok],
Crowd Density Estimation Using Multi-class Adaboost,
AVSS12(447-451).
IEEE DOI
1211
BibRef
Zhang, Z.[Zhong],
Yin, W.H.[Wei-Hong],
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Fast Crowd Density Estimation in Surveillance Videos without Training,
AVSS12(452-457).
IEEE DOI
1211
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Su, H.[Hang],
Yang, H.[Hua],
Zheng, S.[Shibao],
The Large-Scale Crowd Density Estimation Based on Effective Region
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ACCV10(III: 302-313).
Springer DOI
1011
BibRef
Xing, J.L.[Jun-Liang],
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1009
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Earlier:
Human detection in a challenging situation,
ICIP09(2561-2564).
IEEE DOI
0911
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Li, M.[Min],
Zhang, Z.X.[Zhao-Xiang],
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0910
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And:
Rapid and robust human detection and tracking based on omega-shape
features,
ICIP09(2545-2548).
IEEE DOI
0911
BibRef
Earlier:
Estimating the number of people in crowded scenes by MID based
foreground segmentation and head-shoulder detection,
ICPR08(1-4).
IEEE DOI
0812
See also Robust automated ground plane rectification based on moving vehicles for traffic scene surveillance.
BibRef
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0710
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CIAP07(506-511).
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AVSBS06(70-70).
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0606
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ICPR06(III: 1187-1190).
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Earlier:
Counting Pedestrians in Crowds Using Viewpoint Invariant Training,
BMVC05(xx-yy).
HTML Version.
0509
BibRef
Yang, D.B.,
Gonzalez-Banos, H.H.,
Counting people in crowds with a real-time network of simple image
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Detection of Crowds of People by Use of Wavelet Features and
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Khoudour, L.,
Deparis, J.P.,
Bruyelle, J.L.,
Cabestaing, F.,
Aubert, D.,
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Crowd density using flow.
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Crosswalk Detection, Zebra Crossings .