16.7.4.2.8 Counting People, Crowds, Crowd Counting

Chapter Contents (Back)
Counting People. Crowd Counting.

Chen, Z.[Zhuo], Wang, L.[Lu], Yung, N.H.C.[Nelson H.C.],
Adaptive human motion analysis and prediction,
PR(44), No. 12, December 2011, pp. 2902-2914.
Elsevier DOI 1107
Motion pattern; Pattern clustering; Pattern classification; Prediction BibRef

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
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


Rodriguez, A.C.[Andres C.], Wegner, J.D.[Jan D.],
Counting the Uncountable: Deep Semantic Density Estimation from Space,
GCPR18(351-362).
Springer DOI 1905
BibRef

Hossain, M., Hosseinzadeh, M., Chanda, O., Wang, Y.,
Crowd Counting Using Scale-Aware Attention Networks,
WACV19(1280-1288)
IEEE DOI 1904
learning (artificial intelligence), neural net architecture, object detection, crowded scene, crowd density, crowd counting, Computational modeling BibRef

Chen, X., Bin, Y., Sang, N., Gao, C.,
Scale Pyramid Network for Crowd Counting,
WACV19(1941-1950)
IEEE DOI 1904
computer vision, object detection, pedestrians, traffic engineering computing, Scale Pyramid Module, Computer vision BibRef

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 .


Last update:Oct 1, 2019 at 15:23:24