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1107
Motion pattern; Pattern clustering; Pattern classification; Prediction
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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
BibRef
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
BibRef
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
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
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.[Yanyan],
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
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.[Xinlei],
Du, J.P.[Jun-Ping],
Liang, M.[Meiyu],
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
Crols, T.[Tomas],
Malleson, N.[Nick],
Quantifying the ambient population using hourly population footfall
data and an agent-based model of daily mobility,
GeoInfo(23), No. 2, Apriul 2019, pp. 201-220.
WWW 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
Hossain, M.,
Hosseinzadeh, M.,
Chanda, O.,
Wang, Y.,
Crowd Counting Using Scale-Aware Attention Networks,
WACV19(1280-1288)
IEEE DOI
1904
learning (artificial intelligence), neural net architecture,
object detection, crowded scene, crowd density, crowd counting,
Computational modeling
BibRef
Chen, X.,
Bin, Y.,
Sang, N.,
Gao, C.,
Scale Pyramid Network for Crowd Counting,
WACV19(1941-1950)
IEEE DOI
1904
computer vision, object detection, pedestrians,
traffic engineering computing, Scale Pyramid Module,
Computer vision
BibRef
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
Zhang, Y.Y.[Ying-Ying],
Zhou, D.[Desen],
Chen, S.Q.[Si-Qin],
Gao, S.H.[Sheng-Hua],
Ma, Y.[Yi],
Single-Image Crowd Counting via Multi-Column Convolutional Neural
Network,
CVPR16(589-597)
IEEE DOI
1612
BibRef
Shi, Z.,
Zhang, L.,
Liu, Y.,
Cao, X.,
Ye, Y.,
Cheng, M.,
Zheng, G.,
Crowd Counting with Deep Negative Correlation Learning,
CVPR18(5382-5390)
IEEE DOI
1812
Training, Correlation, Computer vision, Decorrelation, Testing,
Complexity theory, Visualization
BibRef
Shen, Z.,
Xu, Y.,
Ni, B.,
Wang, M.,
Hu, J.,
Yang, X.,
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit,
CVPR18(5245-5254)
IEEE DOI
1812
Estimation, Feature extraction, Training, Task analysis, Kernel,
Generators, Switches
BibRef
Liu, J.,
Gao, C.,
Meng, D.,
Hauptmann, A.G.,
DecideNet: Counting Varying Density Crowds Through Attention Guided
Detection and Density Estimation,
CVPR18(5197-5206)
IEEE DOI
1812
Estimation, Detectors, Reliability, Head, Computer vision,
Task analysis, Visualization
BibRef
Marsden, M.,
McGuinness, K.,
Little, S.,
Keogh, C.E.,
O'Connor, N.E.,
People, Penguins and Petri Dishes: Adapting Object Counting Models to
New Visual Domains and Object Types Without Forgetting,
CVPR18(8070-8079)
IEEE DOI
1812
Visualization, Task analysis, Training, Adaptation models, Wildlife,
Computer architecture, Convolutional neural networks
BibRef
Liu, X.,
van de Weijer, J.,
Bagdanov, A.D.,
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank,
CVPR18(7661-7669)
IEEE DOI
1812
Task analysis, Training, Computer vision, Visualization, Estimation,
Head, Context modeling
BibRef
Yang, J.,
Zhou, Y.,
Kung, S.,
Multi-scale Generative Adversarial Networks for Crowd Counting,
ICPR18(3244-3249)
IEEE DOI
1812
Generators, Feature extraction, Estimation, Convolution,
Generative adversarial networks, Task analysis, Training
BibRef
Idrees, H.[Haroon],
Tayyab, M.[Muhmmad],
Athrey, K.[Kishan],
Zhang, D.[Dong],
Al-Maadeed, S.[Somaya],
Rajpoot, N.[Nasir],
Shah, M.[Mubarak],
Composition Loss for Counting, Density Map Estimation and Localization
in Dense Crowds,
ECCV18(II: 544-559).
Springer DOI
1810
BibRef
Laradji, I.H.[Issam H.],
Rostamzadeh, N.[Negar],
Pinheiro, P.O.[Pedro O.],
Vazquez, D.[David],
Schmidt, M.[Mark],
Where Are the Blobs: Counting by Localization with Point Supervision,
ECCV18(II: 560-576).
Springer DOI
1810
BibRef
Ranjan, V.[Viresh],
Le, H.[Hieu],
Hoai, M.[Minh],
Iterative Crowd Counting,
ECCV18(VII: 278-293).
Springer DOI
1810
BibRef
Cao, X.K.[Xin-Kun],
Wang, Z.P.[Zhi-Peng],
Zhao, Y.Y.[Yan-Yun],
Su, F.[Fei],
Scale Aggregation Network for Accurate and Efficient Crowd Counting,
ECCV18(VI: 757-773).
Springer DOI
1810
BibRef
Amirgholipour, S.,
He, X.,
Jia, W.,
Wang, D.,
Zeibots, M.,
A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting,
ICIP18(948-952)
IEEE DOI
1809
Head, Adaptation models, Training, Linguistics, Estimation, Testing,
Australia, Crowd counting, Scale Variation, Adaptive Counting CNN
BibRef
Liu, M.,
Liu, Y.,
Jiang, J.,
Guo, Z.,
Wang, Z.,
Crowd Counting with Fully Convolutional Neural Network,
ICIP18(953-957)
IEEE DOI
1809
Estimation, Testing, Training, Kernel, Feature extraction,
Convolutional neural networks, Task analysis, Crowd counting,
deep learning
BibRef
Küchhold, M.,
Simon, M.,
Eiselein, V.,
Sikora, T.,
Scale-Adaptive Real-Time Crowd Detection and Counting for Drone
Images,
ICIP18(943-947)
IEEE DOI
1809
Image segmentation, Drones, Feature extraction, Image resolution,
Cameras, Real-time systems, Kernel, crowd counting, crowd detection,
surveillance
BibRef
Cao, J.M.[Jin-Meng],
Yang, B.[Biao],
Zhang, Y.Y.[Yu-Yu],
Zou, L.[Ling],
Crowd Counting from a Still Image Using Multi-scale Fully Convolutional
Network with Adaptive Human-Shaped Kernel,
PSIVTWS17(227-240).
Springer DOI
1806
BibRef
Pai, A.K.,
Karunakar, A.K.,
Raghavendra, U.,
A Novel Crowd Density Estimation Technique using Local Binary Pattern
and Gabor Features,
AVSS17(1-6)
IEEE DOI
1806
Gabor filters, computer vision, feature extraction,
image representation, image texture, pattern classification,
Video surveillance
BibRef
Vandoni, J.,
Aldea, E.,
Le Hégarat-Mascle, S.,
Active learning for high-density crowd count regression,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, image recognition,
learning (artificial intelligence), object detection,
Training
BibRef
Jiang, H.,
Jin, W.,
Yu, Z.,
Xu, P.,
Combing spatial and temporal features for crowd counting with point
supervision,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, image motion analysis, object detection,
video signal processing, crowd counting map, crowd density map,
Vegetation
BibRef
Sindagi, V.A.[Vishwanath A.],
Patel, V.M.[Vishal M.],
CNN-Based cascaded multi-task learning of high-level prior and
density estimation for crowd counting,
AVSS17(1-6)
IEEE DOI
1806
convolution, image classification,
learning (artificial intelligence), neural nets,
Training
BibRef
Marsden, M.,
McGuinness, K.,
Little, S.,
O'Connor, N.E.,
ResnetCrowd: A residual deep learning architecture for crowd
counting, violent behaviour detection and crowd density level
classification,
AVSS17(1-7)
IEEE DOI
1806
computer vision, estimation theory, feature extraction,
image classification, learning (artificial intelligence),
Urban areas
BibRef
Fan, C.,
Tang, J.,
Wang, N.,
Liang, D.,
Rich Convolutional Features Fusion for Crowd Counting,
FG18(394-398)
IEEE DOI
1806
Computer architecture, Estimation, Feature extraction,
Heating systems, Robustness, Task analysis, Training, CNN,
features fusion
BibRef
Zhang, L.,
Shi, M.,
Chen, Q.,
Crowd Counting via Scale-Adaptive Convolutional Neural Network,
WACV18(1113-1121)
IEEE DOI
1806
computer vision, feature extraction, image classification,
learning (artificial intelligence), neural nets,
Training
BibRef
Olmschenk, G.,
Tang, H.,
Zhu, Z.,
Crowd Counting with Minimal Data Using Generative Adversarial
Networks for Multiple Target Regression,
WACV18(1151-1159)
IEEE DOI
1806
feedforward neural nets, inference mechanisms,
learning (artificial intelligence), object recognition,
Training
BibRef
Zeng, L.,
Xu, X.,
Cai, B.,
Qiu, S.,
Zhang, T.,
Multi-scale convolutional neural networks for crowd counting,
ICIP17(465-469)
IEEE DOI
1803
Convolutional neural networks, Feature extraction, Kernel,
Optimization, Robustness, Training, Multi-scale CNN, crowd counting,
scale-relevant architectures
BibRef
Xiong, F.,
Shi, X.,
Yeung, D.Y.,
Spatiotemporal Modeling for Crowd Counting in Videos,
ICCV17(5161-5169)
IEEE DOI
1802
image sequences, learning (artificial intelligence), neural nets,
regression analysis, video signal processing, CNN,
Videos
BibRef
Sam, D.B.,
Surya, S.,
Babu, R.V.,
Switching Convolutional Neural Network for Crowd Counting,
CVPR17(4031-4039)
IEEE DOI
1711
Computer architecture, Head, Neural networks,
Relays, Switches, Training
BibRef
Wang, T.[Tao],
Li, G.[Guohui],
Lei, J.[Jun],
Li, S.[Shuohao],
Xu, S.[Shukui],
Crowd Counting Based on MMCNN in Still Images,
SCIA17(I: 468-479).
Springer DOI
1706
BibRef
Elassal, N.[Nada],
Elder, J.H.[James H.],
Unsupervised Crowd Counting,
ACCV16(V: 329-345).
Springer DOI
1704
BibRef
Siva, P.,
Shafiee, M.J.,
Jamieson, M.,
Wong, A.,
Real-Time, Embedded Scene Invariant Crowd Counting Using
Scale-Normalized Histogram of Moving Gradients (HoMG),
ECVW16(885-892)
IEEE DOI
1612
BibRef
Shang, C.,
Ai, H.,
Bai, B.,
End-to-end crowd counting via joint learning local and global count,
ICIP16(1215-1219)
IEEE DOI
1610
Computational modeling
BibRef
Zalluhoglu, C.[Cemil],
Ikizler-Cinbis, N.[Nazli],
Counting People in Crowded Scenes via Detection and Regression Fusion,
ICIAR16(309-317).
Springer DOI
1608
BibRef
Khan, U.[Usman],
Klette, R.[Reinhard],
Logarithmically Improved Property Regression for Crowd Counting,
PSIVT15(123-135).
Springer DOI
1602
BibRef
Yang, R.[Ren],
Xu, H.Z.[Hua-Zhong],
Wang, J.Q.[Jin-Qiao],
Robust Crowd Segmentation and Counting in Indoor Scenes,
MMMod16(I: 505-514).
Springer DOI
1601
BibRef
Zhao, Z.[Zhuoyi],
Li, H.S.[Hong-Sheng],
Zhao, R.[Rui],
Wang, X.G.[Xiao-Gang],
Crossing-Line Crowd Counting with Two-Phase Deep Neural Networks,
ECCV16(VIII: 712-726).
Springer DOI
1611
BibRef
Zhang, C.[Cong],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Yang, X.K.[Xiao-Kang],
Cross-scene crowd counting via deep convolutional neural networks,
CVPR15(833-841)
IEEE DOI
1510
BibRef
Kumagai, S.[Shohei],
Hotta, K.[Kazuhiro],
HLAC between Cells of HOG Feature for Crowd Counting,
ISVC14(I: 688-697).
Springer DOI
1501
BibRef
Pedersen, J.B.,
Markussen, J.B.,
Philipsen, M.P.,
Jensen, M.B.,
Moeslund, T.B.,
Counting the Crowd at a Carnival,
ISVC14(II: 706-715).
Springer DOI
1501
BibRef
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
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
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
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Crosswalk Detection, Zebra Crossings .