16.7.4.2.9 Counting People, Crowds, Crowd Counting

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

Ma, T.J.[Tian-Jun], Ji, Q.G.[Qing-Ge], Li, N.[Ning],
Scene invariant crowd counting using multi-scales head detection in video surveillance,
IET-IPR(12), No. 12, December 2018, pp. 2258-2263.
DOI Link 1812
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.[Mingliang], Ge, Z.Y.[Zhao-Yang], Jiang, X.[Xiaoheng], 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

Li, H.[He], Zhang, S.H.[Shi-Hui], Kong, W.H.[Wei-Hang],
Crowd counting using a self-attention multi-scale cascaded network,
IET-CV(13), No. 6, September 2019, pp. 556-561.
DOI Link 1911
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
BibRef

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
BibRef

Wu, Q.[Qin], Yan, F.F.[Fang-Fang], Chai, Z.[Zhilei], Guo, G.D.[Guo-Dong],
Crowd counting by the dual-branch scale-aware network with ranking loss constraints,
IET-CV(14), No. 3, April 2020, pp. 101-109.
DOI Link 2003
BibRef

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
BibRef

Zhu, M.[Ming], Wang, X.[Xuqing], Tang, J.[Jun], Wang, N.[Nian], Qu, L.[Lei],
Attentive multi-stage convolutional neural network for crowd counting,
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], 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
BibRef

Lei, Y.J.[Yin-Jie], Liu, Y.[Yan], Zhang, P.P.[Ping-Ping], Liu, L.Q.[Ling-Qiao],
Towards using count-level weak supervision for crowd counting,
PR(109), 2021, pp. 107616.
Elsevier DOI 2009
Crowd counting, Count-level annotation, Weak supervision, Auxiliary tasks learning, Asymmetry training BibRef

Gao, J., Wang, Q., Li, X.,
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network,
CirSysVideo(30), No. 10, October 2020, pp. 3486-3498.
IEEE DOI 2010
Estimation, Feature extraction, Image segmentation, Training, Task analysis, Head, Semantics, Crowd counting, crowd analysis, multi-task learning BibRef

Sajid, U., Sajid, H., Wang, H., Wang, G.,
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images,
CirSysVideo(30), No. 10, October 2020, pp. 3499-3512.
IEEE DOI 2010
Estimation, Computational modeling, Benchmark testing, Head, Training, Routing, Crowd counting, crowd density, zooming or normal patch-making blocks BibRef

Zhao, M.M.[Mu-Ming], Zhang, C.Y.[Chong-Yang], Zhang, J.[Jian], Porikli, F.M.[Fatih M.], Ni, B.B.[Bing-Bing], Zhang, W.J.[Wen-Jun],
Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks,
CirSysVideo(30), No. 10, October 2020, pp. 3651-3662.
IEEE DOI 2010
Estimation, Distortion, Cameras, Task analysis, Convolutional neural networks, Australia, Fuses, Crowd counting, scale variation 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
BibRef

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
BibRef

Li, H.[He], Kong, W.H.[Wei-Hang], Zhang, S.H.[Shi-Hui],
Effective crowd counting using multi-resolution context and image quality assessment-guided training,
CVIU(201), 2020, pp. 103065.
Elsevier DOI 2011
Crowd counting, Scale variant, Image quality assessment, Multi-resolution BibRef

Cao, Z.J.[Zhi-Jie], Shamsolmoali, P.[Pourya], Yang, J.[Jie],
Synthetic guided domain adaptive and edge aware network for crowd 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

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, Computer architecture, detection BibRef

Wang, Y.J.[Yong-Jie], Zhang, W.[Wei], Huang, D.X.[Dong-Xiao], Liu, Y.[Yanyan], 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

Wang, L.Y.[Lu-Yang], Li, Y.[Yun], Peng, S.[Sifan], Tang, X.[Xiao], Yin, B.Q.[Bao-Qun],
Multi-level feature fusion network for crowd counting,
IET-CV(15), No. 1, 2021, pp. 60-72.
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
Computer architecture, 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.[Weiqun], 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.[Haoji],
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

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.[Risheng], 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

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.[Hongrun], Zhao, Y.[Yitian], Yang, X.Y.[Xiao-Yun], Qian, X.[Xuesheng], 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

Xu, Y.[Yanyu], Zhong, Z.[Ziming], 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

Chen, B.H.[Bing-Hui], Yan, Z.[Zhaoyi], 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.[Yifan], 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.[Heming], 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.[Yutong], 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.[Minyang], 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

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.[Ziquan], Chan, A.B.[Antoni B.],
A Generalized Loss Function for Crowd Counting and Localization,
CVPR21(1974-1983)
IEEE DOI 2111
Location awareness, Annotations, Computer architecture, Cost function, Bayes methods, Pattern recognition BibRef

Liu, L.[Lingbo], Chen, J.Q.[Jia-Qi], Wu, H.[Hefeng], Li, G.[Guanbin], 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.[Shiqiao], Li, J.[Jiajie], 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
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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
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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, Computer architecture, 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.[Yinjie],
Semi-Supervised Crowd Counting via Self-training on Surrogate Tasks,
ECCV20(XV:242-259).
Springer DOI 2011
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Liu, W.Z.[Wei-Zhe], Salzmann, M.[Mathieu], Fua, P.[Pascal],
Estimating People Flows to Better Count Them in Crowded Scenes,
ECCV20(XV:723-740).
Springer DOI 2011
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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
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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
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Jiang, X., Zhang, L., Xu, M., Zhang, T., Lv, P., Zhou, B., Yang, X., Pang, Y.,
Attention Scaling for Crowd Counting,
CVPR20(4705-4714)
IEEE DOI 2008
Task analysis, Estimation, Kernel, Training, Convolutional neural networks, Feature extraction, Computer vision BibRef

Reddy, M.K.K.[M. K. Krishna], Hossain, M.A.[M. Asiful], Rochan, M., Wang, Y.,
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning,
WACV20(2803-2812)
IEEE DOI 2006
Adaptation models, Training, Cameras, Task analysis, Data models, Surveillance, Training data BibRef

Sajid, U., Wang, G.,
Plug-and-Play Rescaling Based Crowd Counting in Static Images,
WACV20(2276-2285)
IEEE DOI 2006
Estimation, Switches, Detectors, Computer architecture, Image recognition, Benchmark testing, Measurement BibRef

Hossain, M.A., Reddy, M.K.K., Cannons, K., Xu, Z., Wang, Y.,
Domain Adaptation in Crowd Counting,
CRV20(150-157)
IEEE DOI 2006
domain adaptation, crowd counting, few-shot learning, density map BibRef

Phan, C., Hoang, A., Phan, D., Dao, H., Huynh, V.,
Human Density Estimation by Exploiting Deep Spatial Contextual Information,
IVCNZ19(1-5)
IEEE DOI 2004
convolutional neural nets, feature extraction, image capture, image classification, learning (artificial intelligence), Long Short-Term Memory (LSTM) BibRef

Xu, C., Qiu, K., Fu, J., Bai, S., Xu, Y., Bai, X.,
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting,
ICCV19(8381-8389)
IEEE DOI 2004
image motion analysis, image resolution, learning (artificial intelligence), BibRef

Zhang, A., Shen, J., Xiao, Z., Zhu, F., Zhen, X., Cao, X., Shao, L.,
Relational Attention Network for Crowd Counting,
ICCV19(6787-6796)
IEEE DOI 2004
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

Yan, Z., Yuan, Y., Zuo, W., Tan, X., Wang, Y., Wen, S., Ding, E.,
Perspective-Guided Convolution Networks for Crowd Counting,
ICCV19(952-961)
IEEE DOI 2004
Code, Convolutional Networks.
WWW Link. convolutional neural nets, image resolution, object detection, perspective-guided convolution networks, Benchmark testing BibRef

Sindagi, V., Patel, V.,
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting,
ICCV19(1002-1012)
IEEE DOI 2004
feature extraction, image classification, image fusion, multiscale fusion, scale-aware ground-truth density maps, Training BibRef

Wan, J., Chan, A.,
Adaptive Density Map Generation for Crowd Counting,
ICCV19(1130-1139)
IEEE DOI 2004
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
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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).
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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
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Liu, W.Z.[Wei-Zhe], Salzmann, M.[Mathieu], Fua, P.[Pascal],
Context-Aware Crowd Counting,
CVPR19(5094-5103).
IEEE DOI 2002
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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
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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
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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).
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Zhang, Q.[Qi], Chan, A.B.[Antoni B.],
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs,
CVPR19(8289-8298).
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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,
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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, Computer architecture, Training BibRef

Ren, W., Kang, D., Tang, Y., Chan, A.B.,
Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes,
CVPR18(5353-5362)
IEEE DOI 1812
Target tracking, Visualization, Correlation, Estimation, Adaptation models, Object detection, Lighting BibRef

Deb, D., Ventura, J.,
An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting,
Crowd18(308-30809)
IEEE DOI 1812
Convolution, Feature extraction, Training, Aggregates, Kernel, Data mining, Convolutional neural networks BibRef

Jeong, J., Jeong, H., Lim, J., Choi, J., Yun, S., Choi, J.Y.,
Selective Ensemble Network for Accurate Crowd Density Estimation,
ICPR18(320-325)
IEEE DOI 1812
Training, Estimation, Feature extraction, Image resolution, Network architecture, Surveillance, Cameras BibRef

Saqib, M., Daud Khan, S., Blumenstein, M.,
Texture-based feature mining for crowd density estimation: A study,
ICVNZ16(1-6)
IEEE DOI 1701
Cameras BibRef

Xu, B., Qiu, G.,
Crowd density estimation based on rich features and random projection forest,
WACV16(1-8)
IEEE DOI 1606
Computational modeling BibRef

Kaminski, L., Gardzinski, P., Kowalak, K., Mackowiak, S.,
Unsupervised abnormal crowd activity detection in surveillance systems,
WSSIP16(1-4)
IEEE DOI 1608
BibRef
Earlier: A2, A3, A1, A4:
Crowd density estimation based on voxel model in multi-view surveillance systems,
WSSIP15(216-219)
IEEE DOI 1603
image classification BibRef

Pham, V.Q., Kozakaya, T., Yamaguchi, O., Okada, R.,
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation,
ICCV15(3253-3261)
IEEE DOI 1602
Computational modeling BibRef

Zhang, Y.Y.[Ying-Ying], Zhou, D.[Desen], Chen, S.Q.[Si-Qin], Gao, S.H.[Sheng-Hua], Ma, Y.[Yi],
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network,
CVPR16(589-597)
IEEE DOI 1612
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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, Computer architecture, Convolutional neural networks BibRef

Liu, X., van de Weijer, J., Bagdanov, A.D.,
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank,
CVPR18(7661-7669)
IEEE DOI 1812
Task analysis, Training, 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
Computer architecture, Estimation, Feature extraction, Heating systems, Robustness, Task analysis, Training, CNN, features fusion BibRef

Zhang, L., Shi, M., Chen, Q.,
Crowd Counting via Scale-Adaptive Convolutional Neural Network,
WACV18(1113-1121)
IEEE DOI 1806
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 BibRef

Zalluhoglu, C.[Cemil], Ikizler-Cinbis, N.[Nazli],
Counting People in Crowded Scenes via Detection and Regression Fusion,
ICIAR16(309-317).
Springer DOI 1608
BibRef

Khan, U.[Usman], Klette, R.[Reinhard],
Logarithmically Improved Property Regression for Crowd Counting,
PSIVT15(123-135).
Springer DOI 1602
BibRef

Yang, R.[Ren], Xu, H.Z.[Hua-Zhong], Wang, J.Q.[Jin-Qiao],
Robust Crowd Segmentation and Counting in Indoor Scenes,
MMMod16(I: 505-514).
Springer DOI 1601
BibRef

Zhao, Z.[Zhuoyi], Li, H.S.[Hong-Sheng], Zhao, R.[Rui], Wang, X.G.[Xiao-Gang],
Crossing-Line Crowd Counting with Two-Phase Deep Neural Networks,
ECCV16(VIII: 712-726).
Springer DOI 1611
BibRef

Zhang, C.[Cong], Li, H.S.[Hong-Sheng], Wang, X.G.[Xiao-Gang], Yang, X.K.[Xiao-Kang],
Cross-scene crowd counting via deep convolutional neural networks,
CVPR15(833-841)
IEEE DOI 1510
BibRef

Kumagai, S.[Shohei], Hotta, K.[Kazuhiro],
HLAC between Cells of HOG Feature for Crowd Counting,
ISVC14(I: 688-697).
Springer DOI 1501
BibRef

Pedersen, J.B., Markussen, J.B., Philipsen, M.P., Jensen, M.B., Moeslund, T.B.,
Counting the Crowd at a Carnival,
ISVC14(II: 706-715).
Springer DOI 1501
BibRef

Bondi, E.[Enrico], Seidenari, L.[Lorenzo], Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto],
Real-time people counting from depth imagery of crowded environments,
AVSS14(337-342)
IEEE DOI 1411
Cameras 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).
DOI Link 1301
BibRef

Fu, H.Y.[Hui-Yuan], Ma, H.D.[Hua-Dong], Xiao, H.T.[Hong-Tian],
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 classification,
IMVIP12(17-20).
IEEE DOI 1302
BibRef

Scoleri, T., Henneberg, M.,
View-Independent Prediction of Body Dimensions in Crowded Environments,
DICTA12(1-8).
IEEE DOI 1303
BibRef

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], Venetianer, P.L.[Peter L.],
Fast Crowd Density Estimation in Surveillance Videos without Training,
AVSS12(452-457).
IEEE DOI 1211
BibRef

Su, H.[Hang], Yang, H.[Hua], Zheng, S.[Shibao],
The Large-Scale Crowd Density Estimation Based on Effective Region Feature Extraction Method,
ACCV10(III: 302-313).
Springer DOI 1011
BibRef

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,
ICIP11(2061-2064).
IEEE DOI 1201
BibRef

Patzold, M., Evangelio, R.H.[Ruben Heras], Sikora, T.[Thomas],
Counting People in Crowded Environments by Fusion of Shape and Motion Information,
AVSS10(157-164).
IEEE DOI 1009
BibRef

Hou, Y.L.[Ya-Li], Pang, G.K.H.[Grantham K.H.],
Human detection in crowded scenes,
ICIP10(721-724).
IEEE DOI 1009
BibRef
Earlier:
Human detection in a challenging situation,
ICIP09(2561-2564).
IEEE DOI 0911
BibRef

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
BibRef
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

Dong, L.[Lan], Parameswaran, V.[Vasu], Ramesh, V.[Visvanathan], Zoghlami, I.[Imad],
Fast Crowd Segmentation Using Shape Indexing,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Bozzoli, M.[Massimiliano], Cinque, L.[Luigi],
A Statistical Method for People Counting in Crowded Environments,
CIAP07(506-511).
IEEE DOI 0709
BibRef

Sidla, O., Lypetskyy, Y., Brandle, N.[Norbert], Seer, S.[Stefan],
Pedestrian Detection and Tracking for Counting Applications in Crowded Situations,
AVSBS06(70-70).
IEEE DOI 0611
BibRef

Rabaud, V.[Vincent], Belongie, S.J.[Serge J.],
Counting Crowded Moving Objects,
CVPR06(I: 705-711).
IEEE DOI 0606
BibRef

Kong, D.[Dan], Gray, D.[Doug], Tao, H.[Hai],
A Viewpoint Invariant Approach for Crowd Counting,
ICPR06(III: 1187-1190).
IEEE DOI 0609
BibRef
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
BibRef

Faulhaber, D., Niemann, H., Weierich, P.,
Detection of Crowds of People by Use of Wavelet Features and Parameter Free Statistical Models,
MVA98(xx-yy). BibRef 9800

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).
Springer DOI 9709
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 .

Last update:May 15, 2022 at 14:39:14