17.1.3.2.10 Counting People, Crowds, Crowd Counting

Chapter Contents (Back)
Counting People. Crowd Counting.
See also Multi-Scale, Scale Aware Crowd Counting.
See also Multi-Modal Crowd Counting.
See also Counting Instances, Counting Objects.

Marcenaro, L., Marchesotti, L., Regazzoni, C.S.,
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IVC(24), No. 11, 1 November 2006, pp. 1179-1191.
Elsevier DOI 0610
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Earlier:
Tracking and Counting Multiple Interacting People in Indoor Scenes,
PETS02(56-61). 0207
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Morerio, P.[Pietro], Marcenaro, L.[Lucio], Regazzoni, C.S.[Carlo S.],
People Count Estimation In Small Crowds,
AVSS12(476-480).
IEEE DOI 1211
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Marchesotti, L., Piva, S., Regazzoni, C.S.,
An agent-based approach for tracking people in indoor complex environments,
CIAP03(99-102).
IEEE DOI 0310
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Alahi, A.[Alexandre], Jacques, L.[Laurent], Boursier, Y.[Yannick], Vandergheynst, P.[Pierre],
Sparsity Driven People Localization with a Heterogeneous Network of Cameras,
JMIV(41), No. 1-2, September 2011, pp. 39-58.
WWW Link. 1108
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Earlier:
Sparsity-driven people localization algorithm: Evaluation in crowded scenes environments,
PETS-Winter09(1-8).
IEEE DOI 0912
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Lee, G.G.[Gwang-Gook], Kim, W.Y.[Whoi-Yul],
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Zhang, J., Tan, B., Sha, F., He, L.,
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ITS(12), No. 4, December 2011, pp. 1037-1046.
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Sim, C.H.[Chern-Horng], Rajmadhan, E.[Ekambaram], Ranganath, S.[Surendra],
Detecting people in dense crowds,
MVA(23), No. 2, March 2012, pp. 243-253.
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Hung, D.H.[Dao-Huu], Hsu, G.S.[Gee-Sern], Chung, S.L.[Sheng-Luen], Saito, H.[Hideo],
Real-Time Counting People in Crowded Areas by Using Local Empirical Templates and Density Ratios,
IEICE(E95-D), No. 7, July 2012, pp. 1791-1803.
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Earlier: A1, A3, A2, Only:
Local Empirical Templates and Density Ratios for People Counting,
ACCV10(IV: 90-101).
Springer DOI 1011
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Wang, L.[Lu], Yung, N.H.C.[Nelson H.C.],
Three-Dimensional Model-Based Human Detection in Crowded Scenes,
ITS(13), No. 2, June 2012, pp. 691-703.
IEEE DOI 1206
BibRef
Earlier:
Bayesian 3D model based human detection in crowded scenes using efficient optimization,
WACV11(557-563).
IEEE DOI 1101
BibRef
Earlier:
Crowd counting and segmentation in visual surveillance,
ICIP09(2573-2576).
IEEE DOI 0911
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Ge, W.[Weina], Collins, R.T.[Robert T.], Ruback, R.B.[R. Barry],
Vision-Based Analysis of Small Groups in Pedestrian Crowds,
PAMI(34), No. 5, May 2012, pp. 1003-1016.
IEEE DOI 1204
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Earlier:
Automatically detecting the small group structure of a crowd,
WACV09(1-8).
IEEE DOI 0912
Not just single pedestrians, but small groups traveling together. Clustered by proxmimity and velocity. BibRef

Ge, W.[Weina], Collins, R.T.[Robert T.],
Crowd Detection with a Multiview Sampler,
ECCV10(V: 324-337).
Springer DOI 1009
BibRef
Earlier:
Evaluation of sampling-based pedestrian detection for crowd counting,
PETS-Winter09(1-7).
IEEE DOI 0912
Evaluation, Human Detection. BibRef
Earlier:
Marked point processes for crowd counting,
CVPR09(2913-2920).
IEEE DOI 0906
BibRef

Chan, A.B.[Antoni B.], Vasconcelos, N.M.[Nuno M.],
Counting People With Low-Level Features and Bayesian Regression,
IP(21), No. 4, April 2012, pp. 2160-2177.
IEEE DOI 1204
BibRef
Earlier:
Bayesian Poisson regression for crowd counting,
ICCV09(545-551).
IEEE DOI 0909
BibRef

Liu, B., Vasconcelos, N.M.,
Bayesian Model Adaptation for Crowd Counts,
ICCV15(4175-4183)
IEEE DOI 1602
Adaptation models BibRef

Conte, D.[Donatello], Foggia, P.[Pasquale], Percannella, G.[Gennaro], Vento, M.[Mario],
Counting moving persons in crowded scenes,
MVA(24), No. 5, July 2013, pp. 1029-1042.
Springer DOI 1306
BibRef
Earlier:
A Method Based on the Indirect Approach for Counting People in Crowded Scenes,
AVSS10(111-118).
IEEE DOI 1009

See also Graph-Kernel Method for Re-identification, A. BibRef

Conte, D.[Dajana], Foggia, P.[Pasquale], Percannella, G.[Gennaro], Tufano, F.[Francesco], Vento, M.[Mario],
Reflection Removal for People Detection in Video Surveillance Applications,
CIAP11(I: 178-186).
Springer DOI 1109
BibRef
Earlier:
Reflection Removal in Color Videos,
ICPR10(1788-1791).
IEEE DOI 1008
BibRef

Conte, D.[Donatello], Foggia, P.[Pasquale], Percannella, G.[Gennaro], Tufano, F.[Francesco], Vento, M.[Mario],
A Method for Counting People in Crowded Scenes,
AVSS10(225-232).
IEEE DOI 1009
BibRef
And:
Counting Moving People in Videos by Salient Points Detection,
ICPR10(1743-1746).
IEEE DOI 1008
BibRef
Earlier:
An Algorithm for Detection of Partially Camouflaged People,
AVSBS09(340-345).
IEEE DOI 0909

See also Reflection Removal in Color Videos. BibRef

Percannella, G.[Gennaro], Vento, M.[Mario],
A Self-trainable System for Moving People Counting by Scene Partitioning,
ICIAR11(II: 297-306).
Springer DOI 1106
BibRef

Ryan, D.[David], Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Scene invariant multi camera crowd counting,
PRL(44), No. 1, 2014, pp. 98-112.
Elsevier DOI 1407
Crowd counting BibRef

Zhang, X.G.[Xu-Guang], He, H.M.[Hai-Ming], Cao, S.K.[Shu-Kai], Liu, H.H.[Hong-Hai],
Flow field texture representation-based motion segmentation for crowd counting,
MVA(26), No. 7-8, November 2015, pp. 871-883.
WWW Link. 1511
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Ryan, D.[David], Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
An evaluation of crowd counting methods, features and regression models,
CVIU(130), No. 1, 2015, pp. 1-17.
Elsevier DOI 1411
BibRef
Earlier:
Scene Invariant Crowd Counting,
DICTA11(237-242).
IEEE DOI 1205
BibRef
Earlier: A1, A2, A4, A3:
Crowd Counting Using Group Tracking and Local Features,
AVSS10(218-224).
IEEE DOI 1009
BibRef
Earlier: A1, A2, A4, A3:
Crowd Counting Using Multiple Local Features,
DICTA09(81-88).
IEEE DOI 0912

See also Textures of optical flow for real-time anomaly detection in crowds. Crowd counting BibRef

Xu, J.X.[Jing-Xin], Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
Activity Analysis in Complicated Scenes Using DFT Coefficients of Particle Trajectories,
AVSS12(82-87).
IEEE DOI 1211
BibRef
Earlier:
Activity Modelling in Crowded Environments: A Soft-Decision Approach,
DICTA11(107-112).
IEEE DOI 1205
BibRef

Hu, Y.C.[Yao-Cong], Chang, H.[Huan], Nian, F.D.[Fu-Dong], Wang, Y.[Yan], Li, T.[Teng],
Dense Crowd Counting from Still Images with Convolutional Neural Networks,
JVCIR(38), No. 1, 2016, pp. 530-539.
Elsevier DOI 1605
Crowd counting BibRef

Al-Zaydi, Z.Q.H.[Zeyad Q.H.], Ndzi, D.L.[David L.], Yang, Y.Y.[Yan-Yan], Kamarudin, M.L.[Munirah L.],
An adaptive people counting system with dynamic features selection and occlusion handling,
JVCIR(39), No. 1, 2016, pp. 218-225.
Elsevier DOI 1608
Crowd counting BibRef

Ma, Z.[Zheng], Chan, A.B.[Antoni B.],
Counting People Crossing a Line Using Integer Programming and Local Features,
CirSysVideo(26), No. 10, October 2016, pp. 1955-1969.
IEEE DOI 1610
BibRef
Earlier:
Crossing the Line: Crowd Counting by Integer Programming with Local Features,
CVPR13(2539-2546)
IEEE DOI 1309
Cameras. crowd counting; integer programming; local feature; regression BibRef

Gao, L.Q.[Li-Qing], Wang, Y.Z.[Yan-Zhang], Ye, X.[Xin], Wang, J.[Jian],
Crowd counting considering network flow constraints in videos,
IET-IPR(12), No. 1, January 2018, pp. 11-19.
DOI Link 1712
BibRef

Huang, S., Li, X., Zhang, Z., Wu, F., Gao, S., Ji, R., Han, J.,
Body Structure Aware Deep Crowd Counting,
IP(27), No. 3, March 2018, pp. 1049-1059.
IEEE DOI 1801
learning (artificial intelligence), neural nets, object detection, body structure aware deep crowd counting, visual context structure BibRef

Yang, B.[Biao], Cao, J.M.[Jin-Meng], Wang, N.[Nan], Zhang, Y.Y.[Yu-Yu], Zou, L.[Ling],
Counting Challenging Crowds Robustly Using a Multi-Column Multi-Task Convolutional Neural Network,
SP:IC(64), 2018, pp. 118-129.
Elsevier DOI 1804
Crowd counting, Multi-column CNN, Multi-task, Per-scale loss, Density map BibRef

Sindagi, V.A.[Vishwanath A.], Patel, V.M.[Vishal M.],
A survey of recent advances in CNN-based single image crowd counting and density estimation,
PRL(107), 2018, pp. 3-16.
Elsevier DOI 1805
Crowd counting, Density estimation, Crowd analysis BibRef

Sheng, B., Shen, C., Lin, G., Li, J., Yang, W., Sun, C.,
Crowd Counting via Weighted VLAD on a Dense Attribute Feature Map,
CirSysVideo(28), No. 8, August 2018, pp. 1788-1797.
IEEE DOI 1808
Semantics, Feature extraction, Image representation, Encoding, Roads, Neural networks, Image segmentation, Crowd counting, weighted vector of locally aggregated descriptor (W-VLAD) encoder BibRef

Kumagai, S.[Shohei], Hotta, K.[Kazuhiro], Kurita, T.[Takio],
Mixture of counting CNNs,
MVA(29), No. 7, October 2018, pp. 1119-1126.
Springer DOI 1810
For crowds. BibRef

Yudistira, N.[Novanto], Kurita, T.[Takio],
Correlation Net: Spatiotemporal multimodal deep learning for action recognition,
SP:IC(82), 2020, pp. 115731.
Elsevier DOI 2001
Correlation Net, CNN, Activity recognition, Deep learning, Fusion BibRef

Wang, Q., Wan, J., Yuan, Y.,
Deep Metric Learning for Crowdedness Regression,
CirSysVideo(28), No. 10, October 2018, pp. 2633-2643.
IEEE DOI 1811
Feature extraction, Training, Machine learning, Distance measurement, Learning systems, crowd counting BibRef

Wei, X.L.[Xin-Lei], Du, J.P.[Jun-Ping], Liang, M.Y.[Mei-Yu], Ye, L.F.[Ling-Fei],
Boosting deep attribute learning via support vector regression for fast moving crowd counting,
PRL(119), 2019, pp. 12-23.
Elsevier DOI 1902
Deep learning, Boosting learning, Attribute learning, Fast moving crowd, Late fusion, BibRef

Zheng, H., Lin, Z., Cen, J., Wu, Z., Zhao, Y.,
Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation,
CirSysVideo(29), No. 3, March 2019, pp. 787-799.
IEEE DOI 1903
Estimation, Feature extraction, Scalability, Reliability, Cameras, Head, Support vector machines, Pedestrian counting, cross-line counting BibRef

Zhou, Q., Zhang, J., Che, L., Shan, H., Wang, J.Z.,
Crowd Counting With Limited Labeling Through Submodular Frame Selection,
ITS(20), No. 5, May 2019, pp. 1728-1738.
IEEE DOI 1905
Training, Task analysis, Image sequences, Redundancy, Intelligent transportation systems, Feature extraction, Labeling, semi-supervised learning BibRef

Kang, D., Ma, Z., Chan, A.B.,
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks: Counting, Detection, and Tracking,
CirSysVideo(29), No. 5, May 2019, pp. 1408-1422.
IEEE DOI 1905
Feature extraction, Task analysis, Forestry, Estimation, Image resolution, Videos, Measurement, tracking BibRef

Chaudhry, H.[Huma], Rahim, M.S.M.[Mohd Shafry Mohd], Saba, T.[Tanzila], Rehman, A.[Amjad],
Crowd detection and counting using a static and dynamic platform: state of the art,
IJCVR(9), No. 3, 2019, pp. 228-259.
DOI Link 1906
BibRef

Ling, M., Geng, X.,
Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning,
IP(28), No. 11, November 2019, pp. 5691-5701.
IEEE DOI 1909
Videos, Head, Adaptation models, Feature extraction, Cameras, Estimation, Gaussian distribution, Label ambiguity, mixture of Gaussians model BibRef

Miao, Y.Q.[Yun-Qi], Han, J.G.[Jun-Gong], Gao, Y.S.[Yong-Sheng], Zhang, B.C.[Bao-Chang],
ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos,
PRL(125), 2019, pp. 113-118.
Elsevier DOI 1909
Crowd counting, Spatio-temporal feature, Crowd analysis BibRef

Xu, M.L.[Ming-Liang], Ge, Z.Y.[Zhao-Yang], Jiang, X.H.[Xiao-Heng], Cui, G.[Gaoge], Lv, P.[Pei], Zhou, B.[Bing], Xu, C.S.[Chang-Sheng],
Depth Information Guided Crowd Counting for complex crowd scenes,
PRL(125), 2019, pp. 563-569.
Elsevier DOI 1909
Crowd counting, Depth information, Pedestrian detection, Density estimation BibRef

Shami, M.B., Maqbool, S., Sajid, H., Ayaz, Y., Cheung, S.S.,
People Counting in Dense Crowd Images Using Sparse Head Detections,
CirSysVideo(29), No. 9, September 2019, pp. 2627-2636.
IEEE DOI 1909
Head, Feature extraction, Training, Detectors, Support vector machines, Training data, Estimation, head detection BibRef

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 BibRef

Tian, Y., Lei, Y., Zhang, J., Wang, J.Z.,
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 BibRef

Zhao, M.M.[Mu-Ming], Zhang, J.[Jian], Zhang, C.Y.[Chong-Yang], Zhang, W.J.[Wen-Jun],
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting,
CVPR19(12728-12737).
IEEE DOI 2002
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Zhang, W.[Wei], Wang, Y.J.[Yong-Jie], Liu, Y.Y.[Yan-Yan], Zhu, J.H.[Jiang-Hua],
Deep convolution network for dense crowd counting,
IET-IPR(14), No. 4, 27 March 2020, pp. 621-627.
DOI Link 2003
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Nguyen, V.[Vy], Ngo, T.D.[Thanh Duc],
Single-image crowd counting: a comparative survey on deep learning-based approaches,
MultInfoRetr(9), No. 2, June 2020, pp. 63-80.
Springer DOI 2005
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Liu, Y.T.[Yong-Tuo], Wen, Q.[Qiang], Chen, H.X.[Hao-Xin], Liu, W.X.[Wen-Xi], Qin, J.[Jing], Han, G.Q.[Guo-Qiang], He, S.F.[Sheng-Feng],
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 Counting,
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], Yang, J.[Jing], He, L.[Liang],
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

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 for crowd counting,
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 through Ordinal Logistic Regression Analysis,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010
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Cao, Z.J.[Zhi-Jie], Shamsolmoali, P.[Pourya], Yang, J.[Jie],
Synthetic guided domain adaptive and edge aware network for crowd counting,
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

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

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
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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
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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
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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

Zan, C.T.[Chang-Tong], Liu, B.D.[Bao-Di], 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
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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
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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

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
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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.M.[Rui-Mao], 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
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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

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

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
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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

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

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

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.H.[Yue-Hai], Yang, J.[Jing], Chen, B.D.[Ba-Dong], Du, S.Y.[Shao-Yi],
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

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
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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

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

Ling, M.G.[Miao-Gen], Pan, T.H.[Tian-Hang], Ren, Y.[Yi], Wang, K.[Ke], Geng, X.[Xin],
Motional foreground attention-based video crowd counting,
PR(144), 2023, pp. 109891.
Elsevier DOI 2310
Video crowd counting, Frame difference, Attention mechanism BibRef

Wei, X.[Xing], Qiu, Y.F.[Yun-Feng], Ma, Z.H.[Zhi-Heng], Hong, X.P.[Xiao-Peng], Gong, Y.H.[Yi-Hong],
Semi-Supervised Crowd Counting via Multiple Representation Learning,
IP(32), 2023, pp. 5220-5230.
IEEE DOI 2310
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Hou, Y.[Yi], Zhang, S.H.[Shang-Hang], Ma, R.[Rui], Jia, H.Z.[Hui-Zhu], Xie, X.D.[Xiao-Dong],
Frame-Recurrent Video Crowd Counting,
CirSysVideo(33), No. 9, September 2023, pp. 5186-5199.
IEEE DOI 2310
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Savner, S.S.[Siddharth Singh], Kanhangad, V.[Vivek],
Crowd Counting From Limited Labeled Data Using Active Learning,
SPLetters(30), 2023, pp. 1662-1666.
IEEE DOI 2311
BibRef

Cai, Y.Q.[Yi-Qing], Ma, Z.W.[Zhen-Wei], Lu, C.H.[Chang-Hong], Wang, C.B.[Chang-Bo], He, G.[Gaoqi],
Global Representation Guided Adaptive Fusion Network for Stable Video Crowd Counting,
MultMed(25), 2023, pp. 5222-5233.
IEEE DOI 2311
BibRef

Fan, Z.[Zheyi], Song, Z.H.[Zi-Hao], Wu, D.[Di], Zhu, Y.X.[Yi-Xuan],
Multi-branch Segmentation-guided Attention Network for crowd counting,
JVCIR(97), 2023, pp. 103964.
Elsevier DOI 2312
Crowd counting, Multi-task learning, Attention mechanism BibRef

Hao, L.F.[Li-Fei], Huang, B.Q.[Bao-Qi], Jia, B.[Bing], Xu, G.[Gang], Mao, G.Q.[Guo-Qiang],
Toward Accurate Crowd Counting in Large Surveillance Areas Based on Passive WiFi Sensing,
ITS(24), No. 12, December 2023, pp. 14086-14096.
IEEE DOI 2312
BibRef

Wang, R.[Rui], Hao, Y.X.[Yi-Xue], Hu, L.[Long], Li, X.Z.[Xian-Zhi], Chen, M.[Min], Miao, Y.M.[Yi-Ming], Humar, I.[Iztok],
Efficient Crowd Counting via Dual Knowledge Distillation,
IP(33), 2024, pp. 569-583.
IEEE DOI 2401
Computational modeling, Adaptation models, Feature extraction, Task analysis, Knowledge transfer, Loss measurement, Estimation, optimal transport distance BibRef

Wang, X.[Xin], Zhan, Y.[Yue], Zhao, Y.[Yang], Yang, T.[Tangwen], Ruan, Q.Q.[Qiu-Qi],
Hybrid Perturbation Strategy for Semi-Supervised Crowd Counting,
IP(33), 2024, pp. 1227-1240.
IEEE DOI 2402
Perturbation methods, Semantics, Task analysis, Data models, Computational modeling, Training, Semisupervised learning, cross-distribution normalization BibRef

Gong, S.J.[Shen-Jian], Yang, J.[Jian], Zhang, S.S.[Shan-Shan],
Adaptive Teaching for Cross-Domain Crowd Counting,
MultMed(26), 2024, pp. 2943-2952.
IEEE DOI 2402
Adaptation models, Feature extraction, Semisupervised learning, Annotations, Task analysis, Data models, mean teacher BibRef

Jiang, H.Z.[Hang-Zhi], Zhang, X.[Xin], Xiang, S.M.[Shi-Ming],
Non-Maximum Suppression Guided Label Assignment for Object Detection in Crowd Scenes,
MultMed(26), 2024, pp. 2207-2218.
IEEE DOI 2402
Training, Detectors, Object detection, Feature extraction, Annotations, Task analysis, Heuristic algorithms, Object detection, Non-maximum suppression BibRef

Sun, Y.[Yu], Xu, L.B.[Lu-Bing], Bao, Q.[Qian], Liu, W.[Wu], Gao, W.P.[Wen-Peng], Fu, Y.[Yili],
Learning Monocular Regression of 3D People in Crowds via Scene-Aware Blending and De-Occlusion,
MultMed(26), 2024, pp. 2289-2302.
IEEE DOI 2402
Shape, Training, Annotations, Heating systems, Feature extraction, Biological system modeling, Human in Occlusion, De-occlusion BibRef

Zeng, X.[Xin], Wang, H.[Huake], Guo, Q.[Qiang], Wu, Y.P.[Yun-Peng],
Correlation-attention guided regression network for efficient crowd counting,
JVCIR(99), 2024, pp. 104078.
Elsevier DOI 2403
Crowd counting, Crowd density estimation, Attention mechanism, Regression BibRef

Shu, W.[Weibo], Wan, J.[Jia], Chan, A.B.[Antoni B.],
Generalized Characteristic Function Loss for Crowd Analysis in the Frequency Domain,
PAMI(46), No. 5, May 2024, pp. 2882-2899.
IEEE DOI Code:
WWW Link. 2404
Frequency-domain analysis, Task analysis, Training, Noise measurement, Annotations, Location awareness, Head, scene understanding BibRef

Yan, Z.H.[Zi-Heng], Qi, Y.[Yuankai], Li, G.R.[Guo-Rong], Liu, X.[Xinyan], Zhang, W.G.[Wei-Gang], Yang, M.H.[Ming-Hsuan], Huang, Q.M.[Qing-Ming],
Progressive Multi-Resolution Loss for Crowd Counting,
CirSysVideo(34), No. 5, May 2024, pp. 3232-3244.
IEEE DOI Code:
WWW Link. 2405
Annotations, Kernel, Loss measurement, Bayes methods, Costs, Predictive models, Bandwidth, Crowd counting, crowd analysis, cascade loss BibRef

Zhou, W.[Wujie], Yang, X.[Xun], Lei, J.S.[Jing-Sheng], Yan, W.Q.[Wei-Qing], Yu, L.[Lu],
MC3Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting,
ITS(25), No. 5, May 2024, pp. 4156-4165.
IEEE DOI Code:
WWW Link. 2405
Feature extraction, Streams, Estimation, Data mining, Pedestrians, Visualization, Fuses, Complementary attention enhancement, RGB-T crowd counting BibRef

Tang, H.[Haihan], Wang, Y.[Yi], Lin, Z.P.[Zhi-Ping], Chau, L.P.[Lap-Pui], Zhuang, H.P.[Hui-Ping],
A three-stream fusion and self-differential attention network for multi-modal crowd counting,
PRL(183), 2024, pp. 35-41.
Elsevier DOI 2406
Crowd counting, Three-stream fusion, Self-differential attention, Multi-modal data BibRef

Chen, Y.H.[Yue-Hai], Wang, Q.Z.[Qing-Zhong], Yang, J.[Jing], Chen, B.D.[Ba-Dong], Xiong, H.Y.[Hao-Yi], Du, S.Y.[Shao-Yi],
Learning Discriminative Features for Crowd Counting,
IP(33), 2024, pp. 3749-3764.
IEEE DOI 2406
Feature extraction, Location awareness, Transformers, Task analysis, Image reconstruction, Vectors, Object detection, plug-and-play BibRef


Xiong, Z.[Zheng], Chai, L.[Liangyu], Liu, W.X.[Wen-Xi], Liu, Y.[Yongtuo], Ren, S.[Sucheng], He, S.F.[Sheng-Feng],
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting,
WACV24(342-351)
IEEE DOI Code:
WWW Link. 2404
Training, Image resolution, Codes, Labeling, Algorithms, Image recognition and understanding, Algorithms, and algorithms BibRef

Latortue, D.[David], Kdayem, M.[Moetez], Peńa, F.A.G.[Fidel A. Guerrero], Granger, E.[Eric], Pedersoli, M.[Marco],
Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting,
RWSurvil24(290-299)
IEEE DOI Code:
WWW Link. 2404
Location awareness, YOLO, Training, Privacy, Head, Annotations, Detectors BibRef

Chanda, S.[Shekhor], Wang, Y.[Yang],
Dynamic Transfer for Domain Adaptation in Crowd Counting,
MVA23(1-5)
DOI Link 2403
Adaptation models, Computational modeling, Machine vision, Heuristic algorithms, Neural networks, Benchmark testing BibRef

Khan, M.A.[Muhammad Asif], Menouar, H.[Hamid], Hamila, R.[Ridha],
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs,
IVCNZ23(1-6)
IEEE DOI 2403
Visualization, Rain, Neural networks, Estimation, Training data, Generative adversarial networks, Real-time systems, Pix2Pix BibRef

Liu, C.X.[Cheng-Xin], Lu, H.[Hao], Cao, Z.G.[Zhi-Guo], Liu, T.L.[Tong-Liang],
Point-Query Quadtree for Crowd Counting, Localization, and More,
ICCV23(1676-1685)
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, C.[Chen], Hu, X.L.[Xiao-Ling], Abousamra, S.[Shahira], Chen, C.[Chao],
Calibrating Uncertainty for Semi-Supervised Crowd Counting,
ICCV23(16685-16695)
IEEE DOI 2401
BibRef

Huang, Z.K.[Zhi-Kai], Chen, W.T.[Wei-Ting], Chiang, Y.C.[Yuan-Chun], Kuo, S.Y.[Sy-Yen], Yang, M.H.[Ming-Hsuan],
Counting Crowds in Bad Weather,
ICCV23(23251-23262)
IEEE DOI 2401
BibRef

Fotia, L.[Lidia], Percannella, G.[Gennaro], Saggese, A.[Alessia], Vento, M.[Mario],
Highly Crowd Detection and Counting Based on Curriculum Learning,
CAIP23(II:13-22).
Springer DOI 2312
BibRef

Ledda, E.[Emanuele], Delussu, R.[Rita], Putzu, L.[Lorenzo], Fumera, G.[Giorgio], Roli, F.[Fabio],
Blues: Before-relu-estimates Bayesian Inference for Crowd Counting,
CIAP23(II:307-319).
Springer DOI 2312
BibRef

Alfarrarjeh, A.[Abdullah], Kim, S.H.[Seon Ho], Baranwal, U.[Utkarsh], Bitla, Y.[Yash],
Object Detection and Counting Challenges in Real Street Monitoring: Case Study of Homeless Encampments,
ICIP23(2785-2789)
IEEE DOI 2312
BibRef

Tan, X.[Xin], Ishikawa, H.[Hiroshi],
Dataset-Level Directed Image Translation for Cross-Domain Crowd Counting,
ICIP23(400-404)
IEEE DOI 2312
BibRef

Liang, D.K.[Ding-Kang], Xie, J.H.[Jia-Hao], Zou, Z.[Zhikang], Ye, X.Q.[Xiao-Qing], Xu, W.[Wei], Bai, X.[Xiang],
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model,
CVPR23(2893-2903)
IEEE DOI 2309
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Lin, W.[Wei], Chan, A.B.[Antoni B.],
Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting,
CVPR23(21663-21673)
IEEE DOI 2309
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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
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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

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
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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

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

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

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

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

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

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

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

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
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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

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
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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
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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

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

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

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

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
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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
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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

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

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

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

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

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

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

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

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

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],
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SCIA17(I: 468-479).
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Elassal, N.[Nada], Elder, J.H.[James H.],
Unsupervised Crowd Counting,
ACCV16(V: 329-345).
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Shang, C., Ai, H., Bai, B.,
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ICIP16(1215-1219)
IEEE DOI 1610
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ICIAR16(309-317).
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Khan, U.[Usman], Klette, R.[Reinhard],
Logarithmically Improved Property Regression for Crowd Counting,
PSIVT15(123-135).
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Yang, R.[Ren], Xu, H.Z.[Hua-Zhong], Wang, J.Q.[Jin-Qiao],
Robust Crowd Segmentation and Counting in Indoor Scenes,
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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
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Zhang, C.[Cong], Li, H.S.[Hong-Sheng], Wang, X.G.[Xiao-Gang], Yang, X.K.[Xiao-Kang],
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CVPR15(833-841)
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Kumagai, S.[Shohei], Hotta, K.[Kazuhiro],
HLAC between Cells of HOG Feature for Crowd Counting,
ISVC14(I: 688-697).
Springer DOI 1501
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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
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Bondi, E.[Enrico], Seidenari, L.[Lorenzo], Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto],
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AVSS14(337-342)
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Cameras BibRef

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 BibRef

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 BibRef

Chen, K.[Ke], Loy, C.C.[Chen Change], Gong, S.G.[Shao-Gang], Xiang, T.[Tony],
Feature Mining for Localised Crowd Counting,
BMVC12(21).
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Fu, H.Y.[Hui-Yuan], Ma, H.D.[Hua-Dong], Xiao, H.T.[Hong-Tian],
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ICIP12(2685-2688).
IEEE DOI 1302
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Yogameena, B., Perumal, S.S.[S. Saravana], Packiyaraj, N., Saravanan, P.,
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View-Independent Prediction of Body Dimensions in Crowded Environments,
DICTA12(1-8).
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The Large-Scale Crowd Density Estimation Based on Effective Region Feature Extraction Method,
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Xing, J.L.[Jun-Liang], Ai, H.Z.[Hai-Zhou], Liu, L.W.[Li-Wei], Lao, S.H.[Shi-Hong],
Robust crowd counting using detection flow,
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AVSS10(157-164).
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Human detection in crowded scenes,
ICIP10(721-724).
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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], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Pyramidal Statistics of Oriented Filtering for robust pedestrian detection,
VS09(1153-1160).
IEEE DOI 0910
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And:
Rapid and robust human detection and tracking based on omega-shape features,
ICIP09(2545-2548).
IEEE DOI 0911
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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

Dong, L.[Lan], Parameswaran, V.[Vasu], Ramesh, V.[Visvanathan], Zoghlami, I.[Imad],
Fast Crowd Segmentation Using Shape Indexing,
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A Statistical Method for People Counting in Crowded Environments,
CIAP07(506-511).
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Pedestrian Detection and Tracking for Counting Applications in Crowded Situations,
AVSBS06(70-70).
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Rabaud, V.[Vincent], Belongie, S.J.[Serge J.],
Counting Crowded Moving Objects,
CVPR06(I: 705-711).
IEEE DOI 0606
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Kong, D.[Dan], Gray, D.[Doug], Tao, H.[Hai],
A Viewpoint Invariant Approach for Crowd Counting,
ICPR06(III: 1187-1190).
IEEE DOI 0609
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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 sensors,
ICCV03(122-129).
IEEE DOI 0311
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Faulhaber, D., Niemann, H., Weierich, P.,
Detection of Crowds of People by Use of Wavelet Features and Parameter Free Statistical Models,
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Khoudour, L., Deparis, J.P., Bruyelle, J.L., Cabestaing, F., Aubert, D., Bouchafa, S., Velastin, S.A.[Sergio A.], Vicencio-Silva, M.A., Wherett, M.,
Project CROMATICA,
CIAP97(II: 757-764).
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Crowd density using flow. BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Multi-Scale, Scale Aware Crowd Counting .


Last update:Jul 13, 2024 at 15:27:21