17.1.3.2.9 Counting People, Transportation System Monitoring, Queues

Chapter Contents (Back)
Human Detection. Counting People.
See also Human Detection, People Detection, Pedestrians, Locating.
See also Tracking People, Human Tracking, Pedestrian Tracking.
See also Counting People, Crowds, Crowd Counting.
See also Counting Instances, Counting Objects.

Reveal,
2000. Pedestrian Counting Systems.
WWW Link. Vendor, Pedestrian Tracking. Vendor, Surveillance.

Seki, H.[Hiroshi],
Method and apparatus for detecting the number of persons,
US_Patent5,121,201, Jun 9, 1992
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Suzuki, M.[Masato], Inaba, H.[Hiromi], Nakamura, K.[Kiyoshi], Nakata, N.[Naofumi], Yamani, H.[Hiroaki], Oonuma, N.[Naoto],
Apparatus and methods for detecting number of people waiting in an elevator hall using plural image processing means with overlapping fields of view,
US_Patent5,298,697, Mar 29, 1994
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Suzuki, M.[Masato], Inaba, H.[Hiromi], Takenaga, H.[Hiroshi], Yamazaki, M.[Masachika], Oonuma, N.[Naoto], Nakamura, N.[Niyoshi], Sakai, Y.[Yoshio], Yoneda, K.[Kenji], Nakata, N.[Naofumi], Kasai, S.[Syoji],
Image processing apparatus having apparatus for correcting the image processing,
US_Patent5,182,776, Jan 26, 1993
WWW Link. Count people in elevator by background difference. BibRef 9301

Bartolini, F., Cappellini, V., Mecocci, A.,
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Mecocci, A., Bartolini, F., Cappellini, V.,
Image Sequence Analysis for Counting in Real Time People Getting in and out of a Bus,
SP(35), No. 2, 1994, pp. 105-116. BibRef 9400

Schofield, A.J., Mehta, P.A., Stonham, T.J.,
A System for Counting People in Video Images Using Neural Networks to Identify the Background Scene,
PR(29), No. 8, August 1996, pp. 1421-1428.
Elsevier DOI 9608
BibRef

Khoudour, L., Duvieubourg, L., Deparis, J.P.,
Real-Time Pedestrian Counting by Active Linear Cameras,
JEI(5), No. 4, October 1996, pp. 452-459. 9709
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Huang, J.Z.[Jian-Zhong], Florencio, D.A.F.[Dinei A.F.],
System and method for detecting and analyzing a queue,
US_Patent5,953,055, Sep 14, 1999
WWW Link. BibRef 9909
And: US_Patent6,195,121, Feb 27, 2001
WWW Link. BibRef

Iketani, A.[Akihiko], Nagai, A.[Atsushi], Kuno, Y.[Yoshinori], Shirai, Y.[Yoshiaki],
Real-Time Surveillance System Detecting Persons in Complex Scenes,
RealTimeImg(7), No. 5, October 2001, pp. 433-446.
DOI Link 0110
BibRef
Earlier:
Detecting Persons on Changing Background,
ICPR98(Vol I: 74-76).
IEEE DOI 9808
BibRef
Earlier: A1, A3, Add Shimada, N., A4 Only: CIAP99(1112-1115).
IEEE DOI 9909
BibRef

Nagai, A., Kuno, Y., Shirai, Y.,
Surveillance system based on spatio-temporal information,
ICIP96(II: 593-596).
IEEE DOI 9610
BibRef

Albiol, A., Mora, I., Naranjo, V.,
Real-time High Density People Counter Using Morphological Tools,
ITS(2), No. 4, December 2001, pp. 204-218.
IEEE Abstract. 0402
BibRef
Earlier: A1, A3, A2: ICPR00(Vol IV: 652-655).
IEEE DOI 0009
BibRef

Albiol, A.[Antonio], Albiol, A.[Alberto], Silla, J.[Julia],
Statistical video analysis for crowds counting,
ICIP09(2569-2572).
IEEE DOI 0911
BibRef

Lin, S.F.[Sheng-Fuu], Chen, J.Y.[Jaw-Yeh], Chao, H.X.[Hung-Xin],
Estimation of number of people in crowded scenes using perspective transformation,
SMC-A(31), No. 6, November 2001, pp. 645-654.
IEEE Top Reference. 0202
BibRef

Seow, K.T.[Kiam Tian], Pasquier, M.,
Supervising passenger land-transport systems,
ITS(5), No. 3, September 2004, pp. 165-176.
IEEE Abstract. 0501
BibRef

Pai, C.J.[Chia-Jung], Tyan, H.R.[Hsiao-Rong], Liang, Y.M.[Yu-Ming], Liao, H.Y.M.[Hong-Yuan Mark], Chen, S.W.[Sei-Wang],
Pedestrian detection and tracking at crossroads,
PR(37), No. 5, May 2004, pp. 1025-1034.
Elsevier DOI 0405
BibRef
Earlier: ICIP03(II: 101-104).
IEEE DOI 0312
BibRef

Chen, D.Y.[Duan-Yu], Cannons, K., Tyan, H.R.[Hsiao-Rong], Shih, S.W.[Sheng-Wen], Liao, H.Y.M.,
Spatiotemporal Motion Analysis for the Detection and Classification of Moving Targets,
MultMed(10), No. 8, December 2008, pp. 1578-1591.
IEEE DOI 0905
BibRef

Su, C.W.[Chih-Wen], Liao, H.Y.M.[Hong-Yuan Mark], Liang, Y.M.[Yu-Ming], Tyan, H.R.[Hsiao-Rong],
An RST-Tolerant Shape Descriptor for Object Detection,
ICPR10(766-769).
IEEE DOI 1008
Rotation, Scale, Translation. BibRef

Hsieh, J.W.[Jun-Wei], Hsu, Y.T.[Yung-Tai], Liao, H.Y.M., Chen, C.C.A.[Chih-Chi-Ang],
Video-Based Human Movement Analysis and Its Application to Surveillance Systems,
MultMed(10), No. 3, April 2008, pp. 372-384.
IEEE DOI 0905
BibRef

Munder, S., Gavrila, D.M.[Dariu M.],
An Experimental Study on Pedestrian Classification,
PAMI(28), No. 11, November 2006, pp. 1863-1868.
IEEE DOI
PDF File. 0609
Dataset available:
HTML Version. Dataset, Pedestrians. DaimlerChrysler Res. Investigate global versus local and adaptive versus nonadaptive features. PCA coefficients, Haar wavelets, and local receptive fields (LRFs). SVM, Neural Nets, K-NN classifiers. Combination of SVMs with LRF features performs best. And boosted cascade of Haar wavelets is close. BibRef

Mählisch, M., Oberländer, M., Löhlein, O., Gavrila, D.M., and Ritter, W.,
A Multiple Detector Approach to Low-Resolution FIR Pedestrian Recognition,
IVS05(xx-yy).
PDF File. BibRef 0500

Gavrila, D.M.[Dariu M.], Giebel, J., Munder, S.,
Vision-based pedestrian detection: the PROTECTOR system,
IVS04(13-18).
IEEE DOI 0411
Although promising, not yet ready for prime time. BibRef

Gavrila, D.M.,
Looking at people,
AVSBS07(1-1).
IEEE DOI 0709
BibRef

Munder, S., Schnorr, C., Gavrila, D.M.,
Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models,
ITS(9), No. 2, June 2008, pp. 333-343.
IEEE DOI 0806
BibRef

Giebel, J., Gavrila, D.M., Schnörr, C.,
A Bayesian Framework for Multi-cue 3D Object Tracking,
ECCV04(Vol IV: 241-252).
Springer DOI
PDF File. 0405
Integrate object detection. Apply to pedestrians. BibRef

Gavrila, D.M.[Dariu M.],
Sensor-based Pedestrian Protection,
IEEE_Int_Sys(16), No. 6, 2001, pp. 77-81.
PDF File. BibRef 0100
Earlier:
Pedestrian Detection from a Moving Vehicle,
ECCV00(II: 37-49).
Springer DOI 0003
BibRef

Gavrila, D.M., Munder, S.,
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle,
IJCV(73), No. 1, June 2007, pp. 41-59.
Springer DOI 0702
BibRef

Gavrila, D.M.[Dariu M.], Philomin, V.,
Real-Time Object Detection for Smart Vehicles,
ICCV99(87-93).
IEEE DOI
PDF File. BibRef 9900

Heikkilä, J.[Janne], Silvén, O.[Olli],
A Real-Time System for Monitoring of Cyclists and Pedestrians,
IVC(22), No. 7, July 2004, pp. 563-570.
Elsevier DOI 0405
BibRef
Earlier: VS99(xx-yy). BibRef

Heikkila, J.[Janne], Silven, O.[Olli],
Linear Motion Estimation for Image Sequence Based Accurate 3-D Measurements,
ICPR98(Vol II: 1247-1250).
IEEE DOI 9808
BibRef

Bird, N.D., Masoud, O.T., Papanikolopoulos, N.P., Isaacs, A.,
Detection of Loitering Individuals in Public Transportation Areas,
ITS(6), No. 2, June 2005, pp. 167-177.
IEEE Abstract. 0506
BibRef

Velastin, S.A.[Sergio A.], Boghossian, B., Lo, B., Sun, J., Vicencio-Silva, M.A.,
PRISMATICA: Toward Ambient Intelligence in Public Transport Environments,
SMC-A(35), No. 1, January 2005, pp. 164-182.
IEEE Abstract. 0501
BibRef

Black, J., Velastin, S.A.[Sergio A.], Boghossian, B.[Boghos],
A real time surveillance system for metropolitan railways,
AVSBS05(189-194).
IEEE DOI 0602
BibRef

Nair, V.[Vinod], Laprise, P.O.[Pierre-Olivier], Clark, J.J.[James J.],
An FPGA-Based People Detection System,
JASP(2005), No. 7, 2005, pp. 1047-1061.
WWW Link. 0603
BibRef

Casas, J.R., Sitjes, A.P., Folch, P.P.,
Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations,
VISP(152), No. 3, June 2005, pp. 334-346.
DOI Link 0510
BibRef

Ramaswamy, A.[Arun], Nelson, D.J.[Daniel J.], Srinivasan, V.[Venugopal],
Methods and apparatus to count people appearing in an image,
US_Patent7,203,338, Apr 10, 2007
WWW Link. BibRef 0704

Kilambi, P.[Prahlad], Ribnick, E.[Evan], Joshi, A.J.[Ajay J.], Masoud, O.T.[Osama T.], Papanikolopoulos, N.P.[Nikolaos P.],
Estimating pedestrian counts in groups,
CVIU(110), No. 1, April 2008, pp. 43-59.
Elsevier DOI 0804
Groups; Count estimation; Pedestrian tracking; Occlusions; Projection BibRef

Ribnick, E.[Evan], Atev, S.[Stefan], Papanikolopoulos, N.P.[Nikolaos P.],
Estimating 3D Positions and Velocities of Projectiles from Monocular Views,
PAMI(31), No. 5, May 2009, pp. 938-944.
IEEE DOI 0903
BibRef
Earlier: A1, A3, Only:
Estimating 3D Trajectories of Periodic Motions from Stationary Monocular Views,
ECCV08(III: 546-559).
Springer DOI 0810
Localization based on apparent motion in monocular view. BibRef

Ribnick, E.[Evan], Sivalingam, R.[Ravishankar], Papanikolopoulos, N.[Nikolaos], Daniilidis, K.[Kostas],
Reconstructing and analyzing periodic human motion from stationary monocular views,
CVIU(116), No. 7, July 2012, pp. 815-826.
Elsevier DOI 1202
Human motion; 3D reconstruction; Periodicity; Activity classification; Gait analysis BibRef

Xu, S., Duh, H.B.L.,
A Simulation of Bonding Effects and Their Impacts on Pedestrian Dynamics,
ITS(11), No. 1, March 2010, pp. 153-161.
IEEE DOI 1003
BibRef

Gerónimo, D.[David], Sappa, A.D.[Angel D.], Ponsa, D.[Daniel], López, A.M.[Antonio M.],
2D-3D-based on-board pedestrian detection system,
CVIU(114), No. 5, May 2010, pp. 583-595.
Elsevier DOI 1004
BibRef
Earlier: A1, A4, A3, A2:
Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection,
IbPRIA07(I: 418-425).
Springer DOI 0706
Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms BibRef

Gerónimo, D.[David], López, A.M.[Antonio M.], Sappa, A.D.[Angel D.],
Computer Vision Approaches to Pedestrian Detection: Visible Spectrum Survey,
IbPRIA07(I: 547-554).
Springer DOI 0706
BibRef

Geronimo, D.[David], Lopez, A.M.[Antonio M.], Sappa, A.D.[Angel D.], Graf, T.[Thorsten],
Survey of Pedestrian Detection for Advanced Driver Assistance Systems,
PAMI(32), No. 7, July 2010, pp. 1239-1258.
IEEE DOI 1006
Survey, Pedestrian Detection. Driver Assistance. How to deal with the variations in appearance of pedestrians. BibRef

Marin, J.[Javier], Vazquez, D.[David], Geronimo, D.[David], Lopez, A.M.[Antonio M.],
Learning appearance in virtual scenarios for pedestrian detection,
CVPR10(137-144).
IEEE DOI 1006
BibRef

Hou, Y.L., Pang, G.K.H.,
People Counting and Human Detection in a Challenging Situation,
SMC-A(41), No. 1, January 2011, pp. 24-33.
IEEE DOI 1011
BibRef

Tan, B.[Ben], Zhang, J.P.[Jun-Ping], Wang, L.[Liang],
Semi-supervised Elastic net for pedestrian counting,
PR(44), No. 10-11, October-November 2011, pp. 2297-2304.
Elsevier DOI 1101
Semi-supervised regression; Elastic net; Pedestrian counting; Feature selection; Statistical landscape features BibRef

Fernandez Llorca, D., Milanes, V., Parra Alonso, I., Gavilan, M., Garcia Daza, I., Perez, J., Sotelo, M.Á.,
Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller,
ITS(12), No. 2, June 2011, pp. 390-401.
IEEE DOI 1101
BibRef

Yuan, X.[Xue], Wei, X.Y.[Xue-Ye], Song, Y.D.[Yong-Duan],
Pedestrian Detection for Counting Applications Using a Top-View Camera,
IEICE(E94-D), No. 6, June 2011, pp. 1269-1277.
WWW Link. 1101
BibRef

Damen, D.[Dima], Hogg, D.C.[David C.],
Explaining Activities as Consistent Groups of Events: A Bayesian Framework Using Attribute Multiset Grammars,
IJCV(98), No. 1, May 2012, pp. 83-102.
WWW Link. 1204
BibRef
Earlier:
Attribute Multiset Grammars for Global Explanations of Activities,
BMVC09(xx-yy).
PDF File. 0909
BibRef
And:
Recognizing linked events: Searching the space of feasible explanations,
CVPR09(927-934).
IEEE DOI 0906

See also Detecting Carried Objects from Sequences of Walking Pedestrians. BibRef

Garcia-Bunster, G., Torres-Torriti, M., Oberli, C.,
Crowded pedestrian counting at bus stops from perspective transformations of foreground areas,
IET-CV(6), No. 4, 2012, pp. 296-305.
DOI Link 1209

See also Performance Evaluation of UHF RFID Technologies for Real-Time Passenger Recognition in Intelligent Public Transportation Systems. BibRef

Subburaman, V.B.[Venkatesh Bala], Descamps, A.[Adrien], Carincotte, C.[Cyril],
Counting People in the Crowd Using a Generic Head Detector,
AVSS12(470-475).
IEEE DOI 1211
BibRef

Budge, S.E., Sallay, J.A., Wang, Z., Gunther, J.H.,
People Matching for Transportation Planning Using Texel Camera Data for Sequential Estimation,
SMCS(43), No. 3, May 2013, pp. 619-629.
IEEE DOI 1305
BibRef

Cherian, A.[Anoop], Sra, S.[Suvrit], Banerjee, A.[Arindam], Papanikolopoulos, N.P.[Nikolaos P.],
Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices,
PAMI(35), No. 9, 2013, pp. 2161-2174.
IEEE DOI 1307
BibRef
Earlier:
Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence,
ICCV11(2399-2406).
IEEE DOI 1201
Covariance matrix as feature descriptors for people tracking, etc.
See also Efficient Nearest Neighbors via Robust Sparse Hashing. BibRef

Cherian, A.[Anoop], Sra, S.[Suvrit],
Riemannian Sparse Coding for Positive Definite Matrices,
ECCV14(III: 299-314).
Springer DOI 1408
BibRef

Cherian, A.[Anoop], Morellas, V.[Vassilios], Papanikolopoulos, N.P.[Nikolaos P.], Bedros, S.J.[Saad J.],
Dirichlet process mixture models on symmetric positive definite matrices for appearance clustering in video surveillance applications,
CVPR11(3417-3424).
IEEE DOI 1106
BibRef

Sivalingam, R.[Ravishankar], Boley, D.L.[Daniel L.], Morellas, V.[Vassilios], Papanikolopoulos, N.P.[Nikolaos P.],
Tensor Sparse Coding for Positive Definite Matrices,
PAMI(36), No. 3, March 2014, pp. 592-605.
IEEE DOI 1403
BibRef
Earlier:
Positive definite dictionary learning for region covariances,
ICCV11(1013-1019).
IEEE DOI 1201
BibRef
Earlier:
Tensor Sparse Coding for Region Covariances,
ECCV10(IV: 722-735).
Springer DOI 1009
computer vision BibRef

Sivalingam, R., Boley, D., Morellas, V., Papanikolopoulos, N.,
Tensor Dictionary Learning for Positive Definite Matrices,
IP(24), No. 11, November 2015, pp. 4592-4601.
IEEE DOI 1509
Covariance matrices BibRef

Fehr, D.[Duc], Sivalingam, R.[Ravishankar], Morellas, V.[Vassilios], Papanikolopoulos, N.P.[Nikolaos P.], Lotfallah, O.[Osama], Park, Y.C.[Young-Choon],
Counting People in Groups,
AVSBS09(152-157).
IEEE DOI 0909
BibRef

Maddalena, L.[Lucia], Petrosino, A.[Alfredo], Russo, F.[Francesco],
People counting by learning their appearance in a multi-view camera environment,
PRL(36), No. 1, 2014, pp. 125-134.
Elsevier DOI 1312
Artificial neural network BibRef

Reyes, F., Cipriano, A.,
On-line passenger estimation in a metro system using particle filter,
IET-ITS(8), No. 1, February 2014, pp. 1-8.
DOI Link 1406
digital simulation BibRef

Liu, S.B.[Shao-Bo], Lo, S.M.[Siu-Ming], Ma, J.[Jian], Wang, W.L.[Wei-Li],
An Agent-Based Microscopic Pedestrian Flow Simulation Model for Pedestrian Traffic Problems,
ITS(15), No. 3, June 2014, pp. 992-1001.
IEEE DOI 1407
Adaptation models BibRef

Wang, J.Q.[Jin-Qiao], Fu, W.[Wei], Liu, J.J.[Jing-Jing], Lu, H.Q.[Han-Qing],
Spatiotemporal Group Context for Pedestrian Counting,
CirSysVideo(24), No. 9, September 2014, pp. 1620-1630.
IEEE DOI 1410
Markov processes BibRef

Puyol, M.G., Bobkov, D., Robertson, P., Jost, T.,
Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors,
ITS(15), No. 4, August 2014, pp. 1714-1727.
IEEE DOI 1410
autoregressive moving average processes BibRef

Barabino, B., di Francesco, M., Mozzoni, S.,
An Offline Framework for Handling Automatic Passenger Counting Raw Data,
ITS(15), No. 6, December 2014, pp. 2443-2456.
IEEE DOI 1412
data handling BibRef

Barabino, B., di Francesco, M., Mozzoni, S.,
An Offline Framework for the Diagnosis of Time Reliability by Automatic Vehicle Location Data,
ITS(18), No. 3, March 2017, pp. 583-594.
IEEE DOI 1703
Biological system modeling BibRef

Islam, M.K., Vandebona, U., Dixit, V.V., Sharma, A.,
A Bulk Queue Model for the Evaluation of Impact of Headway Variations and Passenger Waiting Behavior on Public Transit Performance,
ITS(15), No. 6, December 2014, pp. 2432-2442.
IEEE DOI 1412
Markov processes BibRef

Islam, M.K., Vandebona, U., Dixit, V.V., Sharma, A.,
A Model to Evaluate the Impact of Headway Variation and Vehicle Size on the Reliability of Public Transit,
ITS(16), No. 4, August 2015, pp. 1840-1850.
IEEE DOI 1508
Analytical models BibRef

Chen, W.G.[Wei-Gang], Wang, X.[Xun], Wang, H.Y.[Hui-Yan], Peng, H.Y.[Hao-Yu],
Hybrid approach using map-based estimation and class-specific Hough forest for pedestrian counting and detection,
IET-IPR(8), No. 12, 2014, pp. 771-781.
DOI Link 1412
Hough transforms BibRef

Tang, N.C., Lin, Y.Y.[Yen-Yu], Weng, M.F.[Ming-Fang], Liao, H.Y.M.,
Cross-Camera Knowledge Transfer for Multiview People Counting,
IP(24), No. 1, January 2015, pp. 80-93.
IEEE DOI 1502
feature extraction BibRef

Zhang, S.S.[Shan-Shan], Bauckhage, C.[Christian], Cremers, A.B.[Armin B.],
Efficient Pedestrian Detection via Rectangular Features Based on a Statistical Shape Model,
ITS(16), No. 2, April 2015, pp. 763-775.
IEEE DOI 1504
Computational modeling BibRef
Earlier:
Informed Haar-Like Features Improve Pedestrian Detection,
CVPR14(947-954)
IEEE DOI 1409
BibRef

Zhang, S.S.[Shan-Shan], Bauckhage, C.[Christian], Klein, D.A.[Dominik A.], Cremers, A.B.[Armin B.],
Exploring Human Vision Driven Features for Pedestrian Detection,
CirSysVideo(25), No. 10, October 2015, pp. 1709-1720.
IEEE DOI 1511
BibRef
Earlier: A1, A3, A2, A4:
Center-Surround Contrast Features for Pedestrian Detection,
ICPR14(2293-2298)
IEEE DOI 1412
Detectors BibRef
Earlier: A1, A2, A3, A4:
Moving pedestrian detection based on motion segmentation,
WORV13(102-107)
IEEE DOI 1307
collision avoidance BibRef

Iryo-Asano, M., Alhajyaseen, W.K.M., Nakamura, H.,
Analysis and Modeling of Pedestrian Crossing Behavior During the Pedestrian Flashing Green Interval,
ITS(16), No. 2, April 2015, pp. 958-969.
IEEE DOI 1504
Analytical models BibRef

Beecroft, M., Pangbourne, K.,
Personal security in travel by public transport: The role of traveller information and associated technologies,
IET-ITS(9), No. 2, 2015, pp. 167-174.
DOI Link 1504
public transport BibRef

Foroughi, H.[Homa], Ray, N.[Nilanjan], Zhang, H.[Hong],
Robust people counting using sparse representation and random projection,
PR(48), No. 10, 2015, pp. 3038-3052.
Elsevier DOI 1507
People counting BibRef

Li, H., Chan, E.C.L., Guo, X., Xiao, J., Wu, K., Ni, L.M.,
Wi-Counter: Smartphone-Based People Counter Using Crowdsourced Wi-Fi Signal Data,
HMS(45), No. 4, August 2015, pp. 442-452.
IEEE DOI 1506
IEEE 802.11 Standards. Not vision based. BibRef

Xia, W.[Wei], Zhang, J.P.[Jun-Ping], Kruger, U.[Uwe],
Semisupervised Pedestrian Counting With Temporal and Spatial Consistencies,
ITS(16), No. 4, August 2015, pp. 1705-1715.
IEEE DOI 1508
Bismuth BibRef

Shan, H.M.[Hong-Ming], Zhang, J.P.[Jun-Ping], Kruger, U.[Uwe],
Framework of Randomized Distribution Features for Visual Representation and Categorization,
Cyber(49), No. 9, Sep. 2019, pp. 3599-3606.
IEEE DOI 1906
Kernel, Visualization, Measurement, Histograms, Resource description framework, Feature extraction, visual representation BibRef

Mukherjee, S.[Satarupa], Gil, S.[Stephani], Ray, N.[Nilanjan],
Unique people count from monocular videos,
VC(31), No. 10, October 2015, pp. 1405-1417.
WWW Link. 1509
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Vera, P.[Pablo], Monjaraz, S.[Sergio], Salas, J.[Joaquín],
Counting pedestrians with a zenithal arrangement of depth cameras,
MVA(27), No. 2, February 2016, pp. 303-315.
WWW Link. 1602
BibRef

Chen, K., Kämäräinen, J.K.,
Pedestrian Density Analysis in Public Scenes With Spatiotemporal Tensor Features,
ITS(17), No. 7, July 2016, pp. 1968-1977.
IEEE DOI 1608
feature extraction BibRef

del Pizzo, L.[Luca], Foggia, P.[Pasquale], Greco, A.[Antonio], Percannella, G.[Gennaro], Vento, M.[Mario],
Counting people by RGB or depth overhead cameras,
PRL(81), No. 1, 2016, pp. 41-50.
Elsevier DOI 1609
People counting BibRef

Zhang, N.Y.[Ning-Yu], Chen, H.J.[Hua-Jun], Chen, X.[Xi], Chen, J.Y.[Jiao-Yan],
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies,
IJGI(5), No. 10, 2016, pp. 180.
DOI Link 1610
BibRef

Guo, Y.Y.[Yan-Yong], Sayed, T.[Tarek], Zaki, M.H.[Mohamed H.],
Automated analysis of pedestrian walking behaviour at a signalised intersection in China,
IET-ITS(11), No. 1, February 2017, pp. 28-36.
DOI Link 1703
BibRef

Chen, K., Zhang, Z.,
Pedestrian Counting With Back-Propagated Information and Target Drift Remedy,
SMCS(47), No. 4, April 2017, pp. 639-647.
IEEE DOI 1704
Cybernetics BibRef

Niu, Q.[Qun], Wu, H.F.[He-Feng], Gao, C.Y.[Cheng-Ying], Luo, X.N.[Xiao-Nan],
Laser-Based Bidirectional Pedestrian Counting via Height Map Guided Regression and Voting,
SIViP(11), No. 5, July 2017, pp. 897-904.
WWW Link. 1706
BibRef

Bartin, B.[Bekir], Ozbay, K.[Kaan], Yang, H.[Hong],
Evaluation framework for mobile ticketing applications in public transit: a case study,
IET-ITS(12), No. 9, November 2018, pp. 1166-1173.
DOI Link 1810
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Kieu, L.M.[Le Minh], Cai, C.[Chen],
Stochastic collective model of public transport passenger arrival process,
IET-ITS(12), No. 9, November 2018, pp. 1027-1035.
DOI Link 1810
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Rajan, P.[Purnima], Ma, Y.M.[Yong-Ming], Jedynak, B.[Bruno],
Cox Processes for Counting by Detection,
JMIV(61), No. 3, March 2019, pp. 380-393.
WWW Link. 1903
BibRef

Grippa, P., Schilcher, U., Bettstetter, C.,
On Access Control in Cabin-Based Transport Systems,
ITS(20), No. 6, June 2019, pp. 2149-2156.
IEEE DOI 1906
Boarding. Access control, Queueing analysis, Numerical stability, Stability criteria, Stochastic processes, boarding BibRef

Denman, S., Fookes, C., Yarlagadda, P.K.D.V., Sridharan, S.,
Scene Invariant Virtual Gates Using DNNs,
CirSysVideo(29), No. 9, September 2019, pp. 2637-2651.
IEEE DOI 1909
Optical imaging, Feature extraction, Cameras, Estimation, Throughput, Logic gates, Person counting, video surveillance BibRef

Sun, S., Akhtar, N., Song, H., Zhang, C., Li, J., Mian, A.,
Benchmark Data and Method for Real-Time People Counting in Cluttered Scenes Using Depth Sensors,
ITS(20), No. 10, October 2019, pp. 3599-3612.
IEEE DOI 1910
Videos, Cameras, Feature extraction, Trajectory, Head, Real-time systems, Cluttered scenes, RGB-D videos BibRef

Tang, L., Zhao, Y., Cabrera, J., Ma, J., Tsui, K.L.,
Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro,
ITS(20), No. 10, October 2019, pp. 3613-3622.
IEEE DOI 1910
Forecasting, Feature extraction, Predictive models, Transportation, Monitoring, Meteorology, Support vector machines, time series BibRef

Cholakkal, H.[Hisham], Sun, G.[Guolei], Khan, F.S.[Fahad Shahbaz], Shao, L.[Ling],
Object Counting and Instance Segmentation With Image-Level Supervision,
CVPR19(12389-12397).
IEEE DOI 2002
BibRef

Ricks, B.[Brian], Dobson, A.[Andrew], Krontiris, A.[Athanasios], Bekris, K.[Kostas], Kapadia, M.[Mubbasir], Roberts, F.[Fred],
Generation of crowd arrival and destination locations/times in complex transit facilities,
VC(36), No. 8, August 2020, pp. 1651-1661.
Springer DOI 2007
BibRef

Ospina, A., Torres, F.,
Countor: count without bells and whistles,
City20(2559-2565)
IEEE DOI 2008
Cameras, Task analysis, Inference algorithms, Target tracking, Urban areas, Detectors BibRef

Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.,
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild,
CVPR20(10384-10393)
IEEE DOI 2008
Task analysis, Feature extraction, Training, Predictive models, Training data BibRef

Yang, J.[Jie], He, W.Y.[Wen Yu], Zhang, T.L.[Tian Lu], Zhang, C.L.[Chun Lei], Zeng, L.[Lu], Nan, B.F.[Bing Fei],
Research on subway pedestrian detection algorithms based on SSD model,
IET-ITS(14), No. 11, November 2020, pp. 1491-1496.
DOI Link 2010
BibRef

Zhou, H.J.[Hui-Juan], Liu, Y.[Yu], Zhao, Y.[Yu],
Passenger inflow control with hierarchical coordination for overloaded metro lines,
IET-ITS(14), No. 11, November 2020, pp. 1418-1425.
DOI Link 2010
BibRef

Liang, Y., Qian, X., Zhu, L.,
Towards Better Railway Service: Passengers Counting in Railway Compartment,
CirSysVideo(31), No. 2, February 2021, pp. 439-451.
IEEE DOI 2102
Cameras, Rail transportation, Task analysis, Proposals, Standards, Head, image processing BibRef

Wang, P.F.[Peng-Fei], Chen, X.W.[Xue-Wu], Chen, J.X.[Jing-Xu], Hua, M.Z.[Ming-Zhuang], Pu, Z.Y.[Zi-Yuan],
A two-stage method for bus passenger load prediction using automatic passenger counting data,
IET-ITS(15), No. 2, 2021, pp. 248-260.
DOI Link 2106
BibRef

Tang, Y.[Yi], Liu, M.[Min], Li, B.[Baopu], Wang, Y.N.[Yao-Nan], Ouyang, W.L.[Wan-Li],
OTP-NMS: Toward Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection,
IP(32), 2023, pp. 3176-3187.
IEEE DOI 2306
Estimation, Correlation, Proposals, Detectors, Task analysis, Standards, Prediction algorithms, Pedestrian detection, gradient estimation BibRef

Jiang, N.[Nan], Yang, L.Z.[Li-Zhong], Yuen, R.K.K.[Richard Kwok Kit], Zhai, C.J.[Chun-Jie],
Modeling the Pedestrian Flow Before Bottleneck Through Learning-Based Method,
ITS(24), No. 7, July 2023, pp. 7035-7047.
IEEE DOI 2307
Behavioral sciences, Microscopy, Trajectory, Predictive models, Neural networks, Computational modeling, Force, Pedestrian flow, machine learning BibRef


Langer, T.[Tim], Waschneck, B.[Bernd], Partzsch, J.[Johannes], Kelber, F.[Florian], Mayr, C.[Christian],
Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different Machine Learning Approaches,
ICPR22(755-761)
IEEE DOI 2212
Energy consumption, Ultrasonic variables measurement, Pulse measurements, Energy measurement, Smart Buildings BibRef

Jiang, N.[Na], Wen, X.S.[Xing-Sen], Shi, Z.P.[Zhi-Ping],
DAPC: Domain Adaptation People Counting via Style-level Transfer Learning and Scene-aware Estimation,
ICPR21(1067-1074)
IEEE DOI 2105
Image analysis, Surveillance, Transfer learning, Estimation, Interference, Pattern recognition, Knowledge transfer, scene-aware estimation BibRef

Wallner, M.[Marco], Steininger, D.[Daniel], Widhalm, V.[Verena], Schörghuber, M.[Matthias], Beleznai, C.[Csaba],
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety,
RISS20(656-671).
Springer DOI 2103
BibRef

Pazzaglia, G.[Giulia], Mameli, M.[Marco], Rossi, L.[Luca], Paolanti, M.[Marina], Mancini, A.[Adriano], Zingaretti, P.[Primo], Frontoni, E.[Emanuele],
People Counting on Low Cost Embedded Hardware During the SARS-COV-2 Pandemic,
DEEPRETAIL20(521-533).
Springer DOI 2103
BibRef

Xie, J.[Jin], Cholakkal, H.[Hisham], Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], Pang, Y.W.[Yan-Wei], Shao, L.[Ling], Shah, M.[Mubarak],
Count- and Similarity-aware R-CNN for Pedestrian Detection,
ECCV20(XVII:88-104).
Springer DOI 2011
BibRef

Lejbølle, A.R., Krogh, B., Nasrollahi, K., Moeslund, T.B.,
One-To-One Person Re-Identification For Queue Time Estimation,
ICIP20(1706-1710)
IEEE DOI 2011
Probes, Cameras, Feature extraction, Time measurement, Airports, Optimization, Queueing analysis, Re-identification, Hungarian algorithm BibRef

Liu, L., He, J., Hou, Y., Zhang, C.,
A Technology for Automatically Counting Bus Passenger Based on YOLOv2 and MIL Algorithm,
ICIVC20(166-170)
IEEE DOI 2009
Target tracking, Streaming media, Classification algorithms, Real-time systems, Cameras, Object detection, Mathematical model, passenger flow statistics BibRef

Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.,
Adaptive Dilated Network With Self-Correction Supervision for Counting,
CVPR20(4593-4602)
IEEE DOI 2008
Feature extraction, Convolution, Estimation, Adaptation models, Labeling, Supervised learning, Gaussian mixture model BibRef

Xiao, F., Liu, H., Lee, Y.J.,
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos,
ICCV19(7012-7021)
IEEE DOI 2004
image classification, unsupervised learning, video signal processing, disentanglement loss, ID input, Public transportation BibRef

Turchini, F.[Francesco], Bruni, M.[Matteo], Baecchi, C.[Claudio], Uricchio, T.[Tiberio], del Bimbo, A.[Alberto],
Open Set Recognition for Unique Person Counting via Virtual Gates,
CIAP19(I:94-105).
Springer DOI 1909
BibRef

Yooyoung, Y.Y.[Yoo-Young], Fiscus, J.[Jon], Godil, A.[Afzal], Joy, D.[David], Delgado, A.[Andrew], Golden, J.[Jim],
ActEV18: Human Activity Detection Evaluation for Extended Videos,
HADCV19(1-8)
IEEE DOI 1902
Artificial intelligence BibRef

van Beeck, K., van Engeland, K., Vennekens, J., Goedemé, T.,
Abnormal behavior detection in LWIR surveillance of railway platforms,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image classification, learning (artificial intelligence), pedestrians, railway safety, Videos BibRef

Farhood, H., He, X., Jia, W., Blumenstein, M., Li, H.,
Counting People Based on Linear, Weighted, and Local Random Forests,
DICTA17(1-7)
IEEE DOI 1804
closed circuit television, feature extraction, image motion analysis, learning (artificial intelligence), Videos BibRef

Cohen, J.P., Boucher, G., Glastonbury, C.A., Lo, H.Z., Bengio, Y.,
Count-ception: Counting by Fully Convolutional Redundant Counting,
BioIm17(18-26)
IEEE DOI 1802
Digital images, Image segmentation, Kernel, Predictive models, Training BibRef

Velastin, S.A.[Sergio A.], Gómez-Lira, D.A.[Diego A.],
People Detection and Pose Classification Inside a Moving Train Using Computer Vision,
IVIC17(319-330).
Springer DOI 1711
BibRef

Soares, G.S.[Guilherme S.], Machado, R.C.[Rubens C.], Lotufo, R.A.[Roberto A.],
People-Flow Counting Using Depth Images for Embedded Processing,
ICIAR17(239-246).
Springer DOI 1706
BibRef

von Borstel, M.[Matthias], Kandemir, M.[Melih], Schmidt, P.[Philip], Rao, M.K.[Madhavi K.], Rajamani, K.[Kumar], Hamprecht, F.A.[Fred A.],
Gaussian Process Density Counting from Weak Supervision,
ECCV16(I: 365-380).
Springer DOI 1611
cells and pedestrians BibRef

Sourtzinos, P.[Panos], Velastin, S.A.[Sergio A.], Jara, M.[Miguel], Zegers, P.[Pablo], Makris, D.[Dimitrios],
People Counting in Videos by Fusing Temporal Cues from Spatial Context-Aware Convolutional Neural Networks,
Crowd16(II: 655-667).
Springer DOI 1611
BibRef

Shimizu, M., Oizumi, J., Matsuoka, R., Takeda, H., Okukura, H., Ooya, A., Koike, A.,
Development Of A Novel System To Measure A Clearance Of A Passenger Platform,
ISPRS16(B5: 573-580).
DOI Link 1610
BibRef

Wang, Y., Zou, Y.,
Fast visual object counting via example-based density estimation,
ICIP16(3653-3657)
IEEE DOI 1610
Estimation BibRef

Kocamaz, M.K., Gong, J., Pires, B.R.,
Vision-based counting of pedestrians and cyclists,
WACV16(1-8)
IEEE DOI 1606
Cameras BibRef

Hung, D.H.[Dao Huu], Saito, H., Yamamoto, K., Sato, H.,
An omnidirectional vision system for bus safety surveillance,
AVSS15(1-6)
IEEE DOI 1511
cameras BibRef

Cunha, P.[Pedro], Moura, D.C.[Daniel C.],
A scalable and privacy preserving approach for counting pedestrians in urban environment,
AVSS15(1-6)
IEEE DOI 1511
Cameras BibRef

Segui, S.[Santi], Pujol, O.[Oriol], Vitria, J.[Jordi],
Learning to count with deep object features,
DeepLearn15(90-96)
IEEE DOI 1510
Accuracy. Counting, not detect and locate individual instances. BibRef

Chen, S.[Sheng], Fern, A.[Alan], Todorovic, S.[Sinisa],
Person count localization in videos from noisy foreground and detections,
CVPR15(1364-1372)
IEEE DOI 1510
BibRef
And:
Multi-object Tracking via Constrained Sequential Labeling,
CVPR14(1130-1137)
IEEE DOI 1409
constraint; multi-object tracking; sequential labeling BibRef

Xu, J.S.[Jing-Song], Wu, Q.A.[Qi-Ang], Zhang, J.[Jian], Silk, B., Ngo, G.T.[Gia Thuan], Tang, Z.M.[Zhen-Min],
Efficient People Counting with Limited Manual Interferences,
DICTA14(1-6)
IEEE DOI 1502
feature extraction BibRef

Hegner, R.[Robert], Hartmann, A.[Andreas], Niederberger, T.[Thomas], Schuster, G.M.[Guido M],
Scalable, self-organizing 3D camera network for non-intrusive people tracking and counting,
ICIP14(3405-3407)
IEEE DOI 1502
Calibration BibRef

Yu, Z.J.[Zhong-Jie], Gong, C.[Chen], Yang, J.[Jie], Bai, L.[Li],
Pedestrian counting based on spatial and temporal analysis,
ICIP14(2432-2436)
IEEE DOI 1502
Bandwidth BibRef

Akai, R.[Ryota], Nitta, N.[Naoko], Babaguchi, N.[Noboru],
Real-Time People Counting across Spatially Adjacent Non-overlapping Camera Views,
MMMod15(I: 71-82).
Springer DOI 1501
BibRef

Luo, J.[Jun], Wang, J.Q.[Jin-Qiao], Xu, H.Z.[Hua-Zhong], Lu, H.Q.[Han-Qing],
A Real-Time People Counting Approach in Indoor Environment,
MMMod15(I: 214-223).
Springer DOI 1501
BibRef

Tabuchi, Y.[Yoshimune], Takahashi, T.[Tomokazu], Deguchi, D.[Daisuke], Ide, I.[Ichiro], Murase, H.[Hiroshi], Kurozumi, T.[Takayuki], Kashino, K.[Kunio],
Spatial People Density Estimation from Multiple Viewpoints by Memory Based Regression,
ICPR14(2209-2214)
IEEE DOI 1412
Cameras BibRef

Mora-Colque, R.V.H.[Rensso V. H.], Cámara-Chávez, G.[Guillermo], Schwartz, W.R.[William Robson],
Detection of Groups of People in Surveillance Videos Based on Spatio-Temporal Clues,
CIARP14(948-955).
Springer DOI 1411
BibRef

Topkaya, I.S.[Ibrahim Saygin], Erdogan, H.[Hakan], Porikli, F.M.[Fatih M.],
Counting people by clustering person detector outputs,
AVSS14(313-318)
IEEE DOI 1411
Clustering algorithms BibRef

Ozer, B.[Burak], Wolf, M.[Marilyn],
A Train Station Surveillance System: Challenges and Solutions,
ECVW14(652-657)
IEEE DOI 1409
gesture recognitionin; surveillance; tracking BibRef

Chua, T.W.[Teck Wee], Leman, K.[Karianto], Gao, F.[Feng],
Hierarchical Audio-Visual Surveillance for Passenger Elevators,
MMMod14(II: 44-55).
Springer DOI 1405
BibRef

Hu, Y.[Yang], Liao, S.C.[Sheng-Cai], Yi, D.[Dong], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
Multi-camera Trajectory Mining: Database and Evaluation,
ICPR14(4684-4689)
IEEE DOI 1412
Cameras BibRef

Noceti, N.[Nicoletta], Odone, F.[Francesca],
Semi-supervised learning of sparse representations to recognize people spatial orientation,
ICIP14(3382-3386)
IEEE DOI 1502
Accuracy BibRef

Zini, L.[Luca], Noceti, N.[Nicoletta], Odone, F.[Francesca],
Precise people counting in real time,
ICIP13(3592-3596)
IEEE DOI 1402
people counting BibRef

Jeong, C.Y.[Chi Yoon], Choi, S.[SuGil], Han, S.W.[Seung Wan],
A method for counting moving and stationary people by interest point classification,
ICIP13(4545-4548)
IEEE DOI 1402
People counting BibRef

Nitta, N.[Naoko], Nakazaki, T.[Takayuki], Nakamura, K.[Kazuaki], Akai, R.[Ryota], Babaguchi, N.[Noboru],
People counting across spatially disjoint cameras by flow estimation between foreground regions,
AVSS13(414-419)
IEEE DOI 1311
Cameras BibRef

Galcík, F.[František], Gargalík, R.[Radoslav],
Real-Time Depth Map Based People Counting,
ACIVS13(330-341).
Springer DOI 1311
BibRef

Casola, V.[Valentina], Esposito, M.[Mariana], Flammini, F.[Francesco], Mazzocca, N.[Nicola],
Performance Evaluation of Video Analytics for Surveillance On-Board Trains,
ACIVS13(414-425).
Springer DOI 1311
BibRef

Zhu, L.[Lei], Wong, K.H.[Kin-Hong],
Human Tracking and Counting Using the KINECT Range Sensor Based on Adaboost and Kalman Filter,
ISVC13(II:582-591).
Springer DOI 1311
BibRef

Roncancio, H.[Henry], Hernandes, A.C.[Andre Carmona], Becker, M.[Marcelo],
Ceiling analysis of pedestrian recognition pipeline for an autonomous car application,
WORV13(215-220)
IEEE DOI 1307
BibRef

Neumann, J.[Joachim], Zao, M.Q.[Man-Qi], Karatzoglou, A.[Alexandros], Oliver, N.[Nuria],
Event Detection in Communication and Transportation Data,
IbPRIA13(827-838).
Springer DOI 1307
BibRef

Nguyen, N.H.[Ngoc Hung], Hartley, R.I.,
Height Measurement for Humans in Motion Using a Camera: A Comparison of Different Methods,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Lin, Y.J.[Yu-Jie], Liu, N.[Ning],
Integrating bottom-up and top-down processes for accurate pedestrian counting,
ICPR12(2508-2511).
WWW Link. 1302
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Li, J.W.[Jing-Wen], Huang, L.[Lei], Liu, C.P.[Chang-Ping],
People Counting across Multiple Cameras for Intelligent Video Surveillance,
AVSS12(178-183).
IEEE DOI 1211
BibRef

Zhang, X.C.[Xu-Cong], Yan, J.J.[Jun-Jie], Feng, S.K.[Shi-Kun], Lei, Z.[Zhen], Yi, D.[Dong], Li, S.Z.[Stan Z.],
Water Filling: Unsupervised People Counting via Vertical Kinect Sensor,
AVSS12(215-220).
IEEE DOI 1211
BibRef

Ogawa, M.[Masahiro], Fukamachi, H.[Hideo], Funayama, R.J.[Ryu-Ji], Kindo, T.[Toshiki],
CYKLS: Detect Pedestrian's Dart Focusing on an Appearance Change,
CVVT12(II: 556-565).
Springer DOI 1210
Driver assistance BibRef

Hsieh, J.W.[Jun-Wei], Fang, F.J.[Fu-Jiang], Lin, G.J.[Guo-Jin], Wang, Y.S.[Yu-Shi],
Template Matching and Monte Carlo Markova Chain for People Counting under Occlusions,
MMMod12(761-771).
Springer DOI 1201
BibRef

Mukherjee, S.[Satarupa], Saha, B.N.[Baidya-Nath], Jamal, I.[Iqbal], Leclerc, R.[Richard], Ray, N.[Nilanjan],
Anovel framework for automatic passenger counting,
ICIP11(2969-2972).
IEEE DOI 1201
BibRef

Li, J.W.[Jing-Wen], Huang, L.[Lei], Liu, C.P.[Chang-Ping],
Online adaptive learning for multi-camera people counting,
ICPR12(3415-3418).
WWW Link. 1302
BibRef
Earlier:
Robust people counting in video surveillance: Dataset and system,
AVSBS11(54-59).
IEEE DOI 1111
BibRef

Rosner, M.[Marcin],
Intelligent crossing sensor and vehicle detector,
AVSBS11(535).
IEEE DOI 1111
AVSS 2011 demo session: BibRef

Leoputra, W.S., Venkatesh, S., Tan, T.[Tele],
Pedestrian detection for mobile bus surveillance,
ICARCV08(726-732).
IEEE DOI 1109
BibRef
And:
Passenger monitoring in moving bus video,
ICARCV08(719-725).
IEEE DOI 1109
BibRef

Lovell, B.C., Chen, S., Bigdeli, A., Berglund, E., Sanderson, C.,
On intelligent surveillance systems and face recognition for mass transport security,
ICARCV08(713-718).
IEEE DOI 1109
BibRef

Déniz-Suárez, O.[Oscar], Castrillón-Santana, M.[Modesto], Lorenzo-Navarro, J.[Javier], Bueno, G.[Gloria], Hernández, M.[Mario],
Fast Classification in Incrementally Growing Spaces,
IbPRIA11(305-312).
Springer DOI 1106
BibRef

Hernández-Sosa, D.[Daniel], Castrillón-Santana, M.[Modesto], Lorenzo-Navarro, J.[Javier],
Multi-sensor People Counting,
IbPRIA11(321-328).
Springer DOI 1106
BibRef

Elmarhomy, A.[Ahmed], Karungaru, S.[Stephen], Terada, K.[Kenji],
A method for counting passersby using time-space image,
FCV11(1-5).
IEEE DOI 1102
BibRef

Patzold, M., Sikora, T.,
Real-time person counting by propagating networks flows,
AVSBS11(66-70).
IEEE DOI 1111
BibRef

Benabbas, Y.[Yassine], Ihaddadene, N.[Nacim], Yahiaoui, T., Urruty, T., Djeraba, C.[Chabane],
Spatio-Temporal Optical Flow Analysis for People Counting,
AVSS10(212-217).
IEEE DOI 1009
BibRef

Merad, D.[Djamel], Aziz, K.E.[Kheir Eddine], Thome, N.[Nicolas],
Fast People Counting Using Head Detection From Skeleton Graph,
AVSS10(151-156).
IEEE DOI 1009
BibRef
And: AVSS10(233-240).
IEEE DOI 1009

See also Person Re-identification Using Appearance Classification. BibRef

Gasparini, L., Manduchi, R., Gottardi, M.,
An Ultra-Low-Power Contrast-Based Integrated Camera Node and its Application as a People Counter,
AVSS10(547-554).
IEEE DOI 1009
BibRef

Miller, P., Liu, W.R.[Wei-Ru], Fowler, C., Zhou, H.Y.[Hui-Yu], Shen, J.L.[Jia-Li], Ma, J.B.[Jian-Bing], Zhang, J.G.[Jian-Guo], Yan, W.Q.[Wei-Qi], McLaughlin, K., Sezer, S.,
Intelligent Sensor Information System For Public Transport: To Safely Go ...,
AVSS10(533-538).
IEEE DOI 1009
BibRef

Szczot, M.[Magdalena], Dannenmann, I.[Iris], Lohlein, O.[Otto],
Incorporating Lane Estimation as Context Source in Pedestrian Recognition Task,
ICPR10(2628-2631).
IEEE DOI 1008
BibRef

Pham, Q.C.[Quoc-Cuong], Lapeyronnie, A.[Agnes], Baudry, C.[Christelle], Lucat, L.[Laurent], Sayd, P.[Patrick], Ambellouis, S.[Sebastien], Sodoyer, D.[David], Flancquart, A.[Amaury], Barcelo, A.C.[Alain-Claude], Heer, F.[Frederic], Ganansia, F.[Fabrice], Delcourt, V.[Vincent],
Audio-video surveillance system for public transportation,
IPTA10(47-53).
IEEE DOI 1007
BibRef

Kembhavi, A.[Aniruddha], Yeh, T.[Tom], Davis, L.S.[Larry S.],
Why Did the Person Cross the Road (There)? Scene Understanding Using Probabilistic Logic Models and Common Sense Reasoning,
ECCV10(II: 693-706).
Springer DOI 1009
BibRef

Schraml, S.[Stephan], Belbachir, A.N.[Ahmed Nabil],
A spatio-temporal clustering method using real-time motion analysis on event-based 3D vision,
VAM10(57-63).
IEEE DOI 1006
BibRef

Schraml, S.[Stephan], Belbachir, A.N.[Ahmed Nabil], Brandle, N.,
A real-time pedestrian classification method for event-based dynamic stereo vision,
ECVW10(93-99).
IEEE DOI 1006
BibRef

Belbachir, A.N., Schraml, S., Brandle, N.,
Real-time classification of pedestrians and cyclists for intelligent counting of non-motorized traffic,
SISM10(45-50).
IEEE DOI 1006
BibRef

Leung, V., Orwell, J.[James], Velastin, S.A.[Sergio A.],
Performance Evaluation of Tracking for Public Transport Surveillance,
BMVA(2010), No. 6, 2010, pp. 1-12.
PDF File. 1209
BibRef
Earlier:
Performance evaluation of re-acquisition methods for public transport surveillance,
ICARCV08(705-712).
IEEE DOI 1109
BibRef

Cao, Y.Z.[Yu-Zhen], Chen, L.S.[Lu-Shi], Jia, S.[Shuo],
An Image Based Detection of Pedestrian Crossing,
CISP09(1-5).
IEEE DOI 0910
BibRef

Antic, B.[Borislav], Letic, D.[Dragan], Culibrk, D.[Dubravko], Crnojevic, V.[Vladimir],
K-means based segmentation for real-time zenithal people counting,
ICIP09(2565-2568).
IEEE DOI 0911
BibRef

Terabayashi, K.[Kenji], Hashimoto, Y.[Yuki], Umeda, K.[Kazunori],
Measurement of Pedestrian Groups Using Subtraction Stereo,
ISVC09(II: 538-549).
Springer DOI 0911
BibRef

Zu, K.[Keju], Liu, F.Q.[Fu-Qiang], Li, Z.P.[Zhi-Peng],
Counting Pedestrian in Crowded Subway Scene,
CISP09(1-4).
IEEE DOI 0910
BibRef

Lu-Ling, Mu, P.[Ping'an], Dai, S.G.[Shu-Guang],
Research of Object Tracking Algorithm Applied in Passenger Flow Statistics in Public Traffic,
CISP09(1-3).
IEEE DOI 0910
BibRef

Ma, J.B.[Jian-Bing], Liu, W.[Weiru], Miller, P.[Paul], Yan, W.Q.[Wei-Qi],
Event Composition with Imperfect Information for Bus Surveillance,
AVSBS09(382-387).
IEEE DOI 0909
BibRef

Zhao, X.[Xi], Delleandrea, E.[Emmanuel], Chen, L.M.[Li-Ming],
A People Counting System Based on Face Detection and Tracking in a Video,
AVSBS09(67-72).
IEEE DOI 0909
BibRef

Cong, Y.[Yang], Gong, H.F.[Hai-Feng], Zhu, S.C.[Song-Chun], Tang, Y.D.[Yan-Dong],
Flow mosaicking: Real-time pedestrian counting without scene-specific learning,
CVPR09(1093-1100).
IEEE DOI 0906
BibRef

Fascioli, A.[Alessandra], Fedriga, R.I.[Rean Isabella], Ghidoni, S.[Stefano],
Vision-based monitoring of pedestrian crossings,
CIAP07(566-574).
IEEE DOI 0709
BibRef

Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A., Chapuis, R., Chausse, F.,
Pedestrian localization and tracking system with Kalman filtering,
IVS04(584-589).
IEEE DOI 0411
BibRef

Naturel, X.[Xavier], Odobez, J.M.[Jean-Marc],
Detecting queues at vending machines: A statistical layered approach,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Carter, N.L.[Nicholas L.], Ferryman, J.M.[James M.],
The SAFEE On-Board Threat Detection System,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Arai, H.[Hiroyuki], Miyagawa, I.[Isao], Koike, H.[Hideki], Haseyama, M.[Miki],
Estimating the number of people in a video sequence via geometrical model,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Li, M.[Min], Zhang, Z.X.[Zhao-Xiang], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Robust visual tracking based on simplified biologically inspired features,
ICIP09(4113-4116).
IEEE DOI 0911
BibRef

Yahiaoui, T.[Tarek], Meurie, C.[Cyril], Khoudour, L.[Louahdi], Cabestaing, F.[François],
A People Counting System Based on Dense and Close Stereovision,
ICISP08(59-66).
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Counting People, Crowds, Crowd Counting .


Last update:Mar 16, 2024 at 20:36:19