G. Acampora, V. Loia, G. Percannella, and M. Vento, Trainable estimators for indirect people counting: A comparative study, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp.139-145, 2011.
DOI : 10.1109/FUZZY.2011.6007637

URL : http://repository.tue.nl/755826

A. Albiol, M. J. Silla, A. Albiol, and J. M. Mossi, Video analysis using corner motion statistics, IEEE International Workshop on PETS, pp.31-37, 2009.

S. Ali and M. Shah, A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-6, 2007.
DOI : 10.1109/CVPR.2007.382977

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.4342

S. Ali and M. Shah, Floor Fields for Tracking in High Density Crowd Scenes, ECCV, pp.1-14, 2008.
DOI : 10.1007/978-3-540-88688-4_1

A. Badii, M. Einig, M. Tiemann, D. Thiemert, and C. Lallah, Visual context identification for privacy-respecting video analytics, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), pp.366-371, 2012.
DOI : 10.1109/MMSP.2012.6343470

M. Bauml and R. Stiefelhagen, Evaluation of local features for person reidentification in image sequences, AVSS, pp.291-296, 2011.

B. Benfold and I. Reid, Stable multi-target tracking in real-time surveillance video, CVPR 2011, pp.3457-3464, 2011.
DOI : 10.1109/CVPR.2011.5995667

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.711-720, 1997.
DOI : 10.1109/34.598228

S. S. Blackman, Multiple hypothesis tracking for multiple target tracking. Aerospace and Electronic Systems Magazine, IEEE, vol.19, issue.1, pp.5-18, 2004.
DOI : 10.1109/maes.2004.1263228

M. D. Breitenstein, F. Reichlin, B. Leibe, E. K. Meier, and L. V. , Robust tracking-by-detection using a detector confidence particle filter, 2009 IEEE 12th International Conference on Computer Vision, p.18, 2010.
DOI : 10.1109/ICCV.2009.5459278

A. Briassouli and I. Kompatsiaris, Spatiotemporally localized new event detection in crowds, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp.928-933, 2011.
DOI : 10.1109/ICCVW.2011.6130351

M. Butenuth, F. Burkert, F. Schmidt, S. Hinz, D. Hartmann et al., Integrating pedestrian simulation, tracking and event detection for crowd analysis, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp.150-157, 2011.
DOI : 10.1109/ICCVW.2011.6130237

A. Cavallaro, Privacy in video surveillance, IEEE SIGNAL PROCESSING MAGA- ZINE, p.85, 2007.

A. B. Chan, Z. S. Liang, and N. Vasconcelos, Privacy preserving crowd monitoring: Counting people without people models or tracking, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2008.
DOI : 10.1109/CVPR.2008.4587569

A. B. Chan, M. Morrow, and N. Vasconcelos, Analysis of crowded scenes using holistic properties, IEEE International Workshop on PETS, p.19, 2009.

A. B. Chan, M. Morrow, and N. Vasconcelos, Analysis of crowded scenes using holistic properties, Proceedings of the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp.9-97, 2009.

A. B. Chan and N. Vasconcelos, Bayesian Poisson regression for crowd counting, 2009 IEEE 12th International Conference on Computer Vision, pp.545-551, 2009.
DOI : 10.1109/ICCV.2009.5459191

D. Y. Chen and P. C. Huang, Motion-based unusual event detection in human crowds, Journal of Visual Communication and Image Representation, vol.22, issue.2, pp.178-186, 2011.
DOI : 10.1016/j.jvcir.2010.12.004

D. Clark and B. Vo, Convergence Analysis of the Gaussian Mixture PHD Filter, IEEE Transactions on Signal Processing, pp.1208-1209, 2007.
DOI : 10.1109/TSP.2006.888886

R. T. Collins, A. J. Lipton, and T. Kanade, Introduction to the special section on video surveillance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.745-746, 2000.
DOI : 10.1109/TPAMI.2000.868676

D. Comaniciu, V. Ramesh, and P. Meer, Real-time tracking of non-rigid objects using mean shift, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.142-149, 2000.
DOI : 10.1109/CVPR.2000.854761

D. Conte, P. Foggia, G. Percannella, F. Tufano, and M. Vento, A Method for Counting People in Crowded Scenes, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp.34-133, 2010.
DOI : 10.1109/AVSS.2010.78

C. Corinna and V. Vladimir, Support-vector networks, Machine Learning, p.136, 1995.

R. Cutler and L. Davis, Robust real-time periodic motion detection, analysis, and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.781-796, 1999.
DOI : 10.1109/34.868681

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

D. Angela and J. Dugelay, People re-identification in camera networks based on probabilistic color histograms, 3DIP 2011 Electronic Imaging Conference on 3D Image Processing and Applications, pp.23-27, 2011.

A. C. Davies, J. H. Yin, and S. A. Velastin, Crowd monitoring using image processing, Electronics & Communication Engineering Journal, vol.7, issue.1, pp.37-47, 1995.
DOI : 10.1049/ecej:19950106

I. R. De-almeida and C. R. Jung, Change Detection in Human Crowds, 2013 XXVI Conference on Graphics, Patterns and Images, p.145
DOI : 10.1109/SIBGRAPI.2013.18

H. Dee and D. Hogg, Detecting inexplicable behaviour The British Machine Vision Association, Proceedings of the British Machine Vision Conference, pp.477-486, 2004.
DOI : 10.5244/c.18.50

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.782

A. R. Dick and M. J. Brooks, Issues in automated visual surveillance, pp.195-204, 2003.

F. Dufaux and T. Ebrahimi, Scrambling for Privacy Protection in Video Surveillance Systems, IEEE Transactions on Circuits and Systems for Video Technology, vol.18, issue.8, pp.1168-1174, 2008.
DOI : 10.1109/TCSVT.2008.928225

H. Edelsbrunner, D. Kirkpatrick, and R. Seidel, On the shape of a set of points in the plane, IEEE Transactions on Information Theory, pp.551-559, 1983.
DOI : 10.1109/TIT.1983.1056714

V. Eiselein, D. Arp, M. Pätzold, and T. Sikora, Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, p.76, 2012.
DOI : 10.1109/AVSS.2012.59

A. Elgammal, D. Harwood, and L. Davis, Non-parametric Model for Background Subtraction, Proceedings of the 6th European Conference on Computer Vision- Part II, ECCV '00, pp.751-767, 2000.
DOI : 10.1007/3-540-45053-X_48

R. Emonet, J. Varadarajan, and J. Odobez, Temporal Analysis of Motif Mixtures Using Dirichlet Processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.1, p.19, 2014.
DOI : 10.1109/TPAMI.2013.100

URL : https://hal.archives-ouvertes.fr/hal-00872018

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. of International Conference on Knowledge Discovery and Data Mining (KDD), pp.226-231, 1996.

R. H. Evangelio and T. Sikora, Complementary background models for the detection of static and moving objects in crowded environments, AVSS, p.17, 2011.

G. Farneback, Two-Frame Motion Estimation Based on Polynomial Expansion, Proc. of 13th Scandinavian Conference on Image Analysis, pp.363-370, 2003.
DOI : 10.1007/3-540-45103-X_50

P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010.
DOI : 10.1109/TPAMI.2009.167

J. Ferryman and A. Shahrokni, PETS2009: Dataset and challenge, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp.1-6, 2009.
DOI : 10.1109/PETS-WINTER.2009.5399556

F. Fleuret, J. Berclaz, R. Lengagne, and P. P. Fua, Multi-camera people tracking with a probabilistic occupancy map, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.18, 2007.
DOI : 10.1109/tpami.2007.1174

G. Luca-foresti, P. Mahonen, and C. S. Regazzoni, Multimedia Video-Based Surveillance Systems: Requirements, Issues and Solutions, p.12, 2000.
DOI : 10.1007/978-1-4615-4327-5

H. Fradi and J. L. Dugelay, Low level crowd analysis using frame-wise normalized feature for people counting, 2012 IEEE International Workshop on Information Forensics and Security (WIFS), p.29, 2012.
DOI : 10.1109/WIFS.2012.6412657

H. Fradi and J. L. Dugelay, People counting system in crowded scenes based on feature regression, EUSIPCO 2012, European Signal Processing Conference, 1926.

H. Fradi and J. L. Dugelay, Robust foreground segmentation using improved Gaussian Mixture Model and optical flow, 2012 International Conference on Informatics, Electronics & Vision (ICIEV), pp.31-35, 2012.
DOI : 10.1109/ICIEV.2012.6317376

C. Garate, P. Bilinski, and F. Bremond, Crowd event recognition using HOG tracker, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp.1-6, 2009.
DOI : 10.1109/PETS-WINTER.2009.5399727

URL : https://hal.archives-ouvertes.fr/inria-00515197

G. Gennari and G. D. Hager, Probabilistic data association methods in visual tracking of groups, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.1063-1069, 2004.
DOI : 10.1109/CVPR.2004.1315257

R. B. Girshick, P. F. Felzenszwalb, and D. Mcallester, Discriminatively trained deformable part models, release 5, p.70

I. Haritaoglu, D. Harwood, and L. S. Davis, W/sup 4/: real-time surveillance of people and their activities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.809-830, 2000.
DOI : 10.1109/34.868683

E. Hayman and J. O. Eklundh, Statistical background subtraction for a mobile observer, Proceedings Ninth IEEE International Conference on Computer Vision, p.13, 2003.
DOI : 10.1109/ICCV.2003.1238315

M. Hofmann, M. Haag, and G. , Unified hierarchical multi-object tracking using global data association, 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), p.17, 2013.
DOI : 10.1109/PETS.2013.6523791

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.4222

D. Hoiem, A. A. Efros, and M. Hebert, Putting Objects in Perspective, International Journal of Computer Vision, vol.57, issue.2, pp.3-15, 2008.
DOI : 10.1007/s11263-008-0137-5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.6201

Y. Hou and G. Pang, Automated people counting at a mass site, IEEE International Conference on Automation and Logistics, pp.464-469, 2008.

Y. L. Hou and G. K. Pang, People Counting and Human Detection in a Challenging Situation, IEEE Transactions on Systems, Man, and Cybernetics, pp.24-33, 2011.
DOI : 10.1109/TSMCA.2010.2064299

C. W. Hsu and C. J. Lin, A comparison of methods for multiclass support vector machines, In IEEE Trans. Neural Networks, vol.13, issue.44, pp.415-425, 2002.

H. Idrees, I. Saleemi, and M. Shah, Multi-source Multi-scale Counting in Extremely Dense Crowd Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, p.113, 2013.
DOI : 10.1109/CVPR.2013.329

N. Ihaddadene and C. Djeraba, Real-time crowd motion analysis, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008.
DOI : 10.1109/ICPR.2008.4761041

URL : https://hal.archives-ouvertes.fr/hal-00812311

M. Isard and J. Maccormick, BraMBLe: a Bayesian multiple-blob tracker, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.34-41, 2001.
DOI : 10.1109/ICCV.2001.937594

J. C. Jacques-junior, S. R. Musse, and C. R. Jung, Crowd Analysis Using Computer Vision Techniques, IEEE Signal Processing Magazine, pp.66-77, 2010.
DOI : 10.1109/MSP.2010.937394

I. T. Jolliffe, Principal component analysis, p.43, 2002.
DOI : 10.1007/978-1-4757-1904-8

P. Kaewtrakulpong and R. Bowden, An improved adaptive background mixture model for realtime tracking with shadow detection, 2nd European Workshop on Advanced Video Based Surveillance Systems, p.118, 2001.

R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

V. Kaltsa, A. Briassouli, I. Kompatsiaris, and M. G. Strintzis, Timely, robust crowd event characterization, 2012 19th IEEE International Conference on Image Processing, pp.2697-2700, 2012.
DOI : 10.1109/ICIP.2012.6467455

K. Keung, L. Y. Xu, and X. Wu, Crowd density estimation using texture analysis and learning, IEEE International Conference on Robotics and Biomimetics, pp.214-219, 2006.

S. M. Khan and M. Shah, A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint, European Conference on Computer Vision, p.18, 2006.
DOI : 10.1007/11744085_11

P. Kilambi, O. Masoud, and N. Papanikolopoulos, Crowd Analysis at Mass Transit Sites, 2006 IEEE Intelligent Transportation Systems Conference, pp.753-758, 2006.
DOI : 10.1109/ITSC.2006.1706832

L. Kratz and K. Nishino, Tracking with local spatio-temporal motion patterns in extremely crowded scenes, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.693-700, 2010.
DOI : 10.1109/CVPR.2010.5540149

H. Ulrich and . Kressel, Pairwise classification and support vector machines, Advances in kernel methods: support vector learning, pp.255-268, 1999.

B. Leibe, K. Schindler, and L. V. , Coupled Detection and Trajectory Estimation for Multi-Object Tracking, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4408936

V. Lempitsky and A. Zisserman, Learning to count objects in images, Advances in Neural Information Processing Systems 23, pp.1324-1332, 2010.

L. Li, W. Huang, I. Y. Gu, and Q. Tian, Foreground object detection from videos containing complex background, Proceedings of the eleventh ACM international conference on Multimedia , MULTIMEDIA '03, pp.121-123, 2003.
DOI : 10.1145/957013.957017

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.8313

M. Li, Z. Zhang, K. Huang, and T. Tan, Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008.
DOI : 10.1109/ICPR.2008.4761705

S. F. Lin, J. Y. Chen, and H. X. Chao, Estimation of number of people in crowded scenes using perspective transformation, IEEE Trans. System, Man, and Cybernetics, pp.645-654, 2001.

W. Lin and Y. Liu, Tracking Dynamic Near-Regular Texture Under Occlusion and Rapid Movements, Lecture Notes in Computer Science, vol.63, issue.1, pp.44-55, 2006.
DOI : 10.1006/cviu.1996.0006

G. David and . Lowe, Distinctive image features from scale-invariant keypoints, In Int. J. Comput. Vision, vol.27, issue.131, pp.91-110, 2004.

R. Ma, L. Li, W. Huang, and Q. Tian, On pixel count based crowd density estimation for visual surveillance, IEEE Conference on Cybernetics and Intelligent Systems, pp.170-173, 2004.

W. Ma, L. Huang, and C. Liu, Advanced Local Binary Pattern Descriptors for Crowd Estimation, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp.958-962, 2008.
DOI : 10.1109/PACIIA.2008.258

W. Ma, L. Huang, and C. Liu, Crowd density analysis using co-occurrence texture features, International Conference on Computer Sciences and Convergence Information Technology, pp.170-175, 2010.

W. Ma, L. Huang, and C. Liu, Estimation of crowd density using image processing, Computer Sciences and Convergence Information Technology, pp.170-175, 2010.

R. P. Mahler, Statistical Multisource-Multitarget Information Fusion, p.75, 2007.
DOI : 10.1201/9781420053098.ch16

R. P. Mahler, Multitarget bayes filtering via first-order multitarget moments. Aerospace and Electronic Systems, IEEE Transactions on, vol.39, issue.4, pp.1152-1178, 2003.
DOI : 10.1109/taes.2003.1261119

A. N. Marana, S. A. Velastin, L. F. Costa, and R. A. Lotufo, Estimation of crowd density using image processing, IEE Colloquium on Image Processing for Security Applications, pp.1-8, 1997.
DOI : 10.1049/ic:19970387

A. N. Marana and V. V. Verona, Wavelet packet analysis for crowd density estimation, Proceedings of the IASTED International Symposia on Applied Informatics, pp.535-540, 2001.

V. Y. Mariano, J. Min, J. Park, R. Kasturi, D. Mihalcik et al., Performance evaluation of object detection algorithms, Object recognition supported by user interaction for service robots, pp.965-969, 2002.
DOI : 10.1109/ICPR.2002.1048198

R. Mehran, A. Oyama, and M. Shah, Abnormal crowd behavior detection using social force model, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.935-942, 2009.
DOI : 10.1109/CVPR.2009.5206641

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.1352

S. Moncrieff, S. Venkatesh, and G. West, Context aware privacy in visual surveillance, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008.
DOI : 10.1109/ICPR.2008.4761616

S. M. Mousavi, S. O. Shahdi, and S. A. Abu-bakar, Crowd estimation using histogram model classification based on improved uniform local binary pattern, International Journal of Computer and Electrical Engineering, vol.4, issue.40, pp.256-259, 2012.
DOI : 10.7763/ijcee.2012.v4.490

M. Muja and D. G. Lowe, Fast approximate nearest neighbors with automatic algorithm configuration, VISAPP International Conference on Computer Vision Theory and Applications, pp.331-340, 2009.

V. Norris, M. Mccahill, and D. Wood, Editorial: The growth of CCTV: a global perspective on the international diffusion of video surveillance in publicly accessible space, pp.110-135, 2004.

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002.
DOI : 10.1109/TPAMI.2002.1017623

N. Paragios and V. Ramesh, A MRF-based approach for real-time subway monitoring, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.1034-1040, 2001.
DOI : 10.1109/CVPR.2001.990644

M. Pätzold and T. Sikora, Real-time person counting by propagating networks flows, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.66-70, 2011.
DOI : 10.1109/AVSS.2011.6027296

A. Polus, J. L. Schofer, and A. Ushpiz, Pedestrian Flow and Level of Service, Journal of Transportation Engineering, vol.109, issue.1, pp.46-56, 1983.
DOI : 10.1061/(ASCE)0733-947X(1983)109:1(46)

A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara, Detecting moving shadows: algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, number 25, pp.918-923, 2003.
DOI : 10.1109/TPAMI.2003.1206520

C. E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, p.133, 2006.
DOI : 10.1162/089976602317250933

M. Redi and B. Mérialdo, A Multimedia Retrieval Framework Based on Automatic Graded Relevance Judgments, 18th International Conference on Multimedia Modeling (MMM), p.45, 2012.
DOI : 10.4218/etrij.02.0102.0103

C. S. Regazzoni and A. Tesei, Distributed data fusion for real-time crowding estimation, Signal Processing, vol.53, issue.1, pp.47-63, 1996.
DOI : 10.1016/0165-1684(96)00075-8

B. Ristic, B. Vo, D. Clark, and B. Vo, A Metric for Performance Evaluation of Multi-Target Tracking Algorithms, IEEE Transactions on Signal Processing, vol.59, issue.7, pp.3452-3457, 2011.
DOI : 10.1109/TSP.2011.2140111

M. Rodriguez, S. Ali, and T. Kanade, Tracking in unstructured crowded scenes, 2009 IEEE 12th International Conference on Computer Vision, pp.1389-1396, 2009.
DOI : 10.1109/ICCV.2009.5459301

M. Rodriguez, I. Laptev, J. Sivic, and J. Audibert, Density-aware person detection and tracking in crowds, 2011 International Conference on Computer Vision, pp.2423-2430, 2011.
DOI : 10.1109/ICCV.2011.6126526

URL : https://hal.archives-ouvertes.fr/hal-00654266

M. Rodriguez, J. Sivic, and I. Laptev, Analysis of Crowded Scenes in Video, Intelligent Video Surveillance Systems, pp.251-272, 2012.
DOI : 10.1002/9781118577851.ch15

URL : https://hal.archives-ouvertes.fr/hal-01053878

M. Rodriguez, J. Sivic, I. Laptev, and J. Audibert, Data-driven crowd analysis in videos, 2011 International Conference on Computer Vision, pp.77-90, 2011.
DOI : 10.1109/ICCV.2011.6126374

URL : https://hal.archives-ouvertes.fr/hal-00654256

E. Rosten, R. Porter, and T. Drummond, Faster and Better: A Machine Learning Approach to Corner Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.1, pp.105-119, 2010.
DOI : 10.1109/TPAMI.2008.275

J. Sun, M. Vicencio-silva, D. Aubert, A. Lemmer, P. Brice et al., D7p: Innovative tools for security in transports, 2003.

S. Saxena, F. Brémond, M. Thonnat, and R. Ma, Crowd Behavior Recognition for Video Surveillance, Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS '08, pp.970-981, 2008.
DOI : 10.1049/ecej:19950106

D. Schuhmacher, B. Vo, and B. Vo, A Consistent Metric for Performance Evaluation of Multi-Object Filters, IEEE Transactions on Signal Processing, vol.56, issue.8, pp.563447-3457, 2008.
DOI : 10.1109/TSP.2008.920469

A. Senior, A. Hampapur, Y. Tian, L. Brown, S. Pankanti et al., Appearance models for occlusion handling, in: International Workshop on Performance Evaluation of Tracking and Surveillance, p.13, 2001.
DOI : 10.1016/j.imavis.2005.06.007

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.909

A. Senior, S. Pankanti, A. Hampapur, L. Brown, Y. Tian et al., Enabling Video Privacy through Computer Vision, IEEE Security and Privacy Magazine, vol.3, issue.3, pp.50-57, 2005.
DOI : 10.1109/MSP.2005.65

T. Senst, V. Eiselein, R. H. Evangelio, and T. Sikora, Robust modified l2 local optical flow estimation and feature tracking, IEEE Workshop on Motion and Video Computing (WMVC), pp.685-690, 2011.
DOI : 10.1109/wacv.2011.5711571

T. Senst, V. Eiselein, and T. Sikora, Robust local optical flow for feature tracking. Transactions on Circuits and Systems for Video Technology, pp.9-2012
DOI : 10.1109/tcsvt.2012.2202070

D. N. Serpanos and A. Papalambrou, Security and Privacy in Distributed Smart Cameras, Proceedings of the IEEE, pp.1678-1687, 2008.
DOI : 10.1109/JPROC.2008.928763

M. Shah, O. Javed, and K. Shafique, Automated Visual Surveillance in Realistic Scenarios, IEEE Multimedia, vol.14, issue.1, pp.30-39, 2007.
DOI : 10.1109/MMUL.2007.3

M. Shah, Visual crowd surveillance is like hydrodynamics, Proceedings of the international conference on Multimedia, MM '10, pp.3-4, 2010.
DOI : 10.1145/1873951.1873954

Y. B. Shalom and E. Tse, Tracking in a cluttered environment with probabilistic data association, Automatica, vol.11, issue.5, pp.451-460, 1975.
DOI : 10.1016/0005-1098(75)90021-7

S. Shan, W. Gao, Y. Chang, B. Cao, and P. Yang, Review the strength of gabor features for face recognition from the angle of its robustness to mis-alignment, International Conference on Pattern Recognition, p.48, 2004.

J. Shi and C. Tomasi, Good features to track, CVPR, pp.593-600, 1994.

Y. Shi, Y. Gao, and R. Wang, Real-Time Abnormal Event Detection in Complicated Scenes, 2010 20th International Conference on Pattern Recognition, pp.3653-3656, 2010.
DOI : 10.1109/ICPR.2010.891

S. Srivastava, K. K. Ng, and E. J. Delp, Crowd flow estimation using multiple visual features for scenes with changing crowd densities, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.60-65, 2011.
DOI : 10.1109/AVSS.2011.6027295

C. Stauffer and W. E. Grimson, Adaptive background mixture models for realtime tracking, IEEE Conference on Computer Vision and Pattern Recognition, pp.246-252, 1999.
DOI : 10.1109/cvpr.1999.784637

R. Stiefelhagen, K. Bernardin, R. Bowers, J. Garofolo, D. Mostefa et al., The CLEAR 2006 Evaluation, Multimodal Technologies for Perception of Humans, pp.1-44, 2007.
DOI : 10.1007/978-3-540-69568-4_1

Y. Tian, R. Feris, and A. Hampapur, Real-time detection of abandoned and removed objects in complex environments, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS, p.14
URL : https://hal.archives-ouvertes.fr/inria-00325775

C. Tomasi and T. Kanade, Detection and tracking of point features, p.56, 1991.

Y. Tsai and . Roger, An efficient and accurate camera calibration technique for 3d machine vision, IEEE Conference on Computer Vision and Pattern Recognition, pp.364-374, 1986.

A. Bala-subburaman-venkatesh, C. Descamps, and . Carincotte, Counting people in the crowd using a generic head detector, AVSS, pp.470-475, 2012.

B. Vo and W. Ma, The Gaussian Mixture Probability Hypothesis Density Filter, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4091-4104, 2006.
DOI : 10.1109/TSP.2006.881190

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.7889

Z. Wang, H. Liu, Y. Qian, and T. Xu, Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix, 2012 IEEE International Conference on Multimedia and Expo Workshops, pp.372-377, 2012.
DOI : 10.1109/ICMEW.2012.71

T. Winkler and B. Rinner, A systematic approach towards user-centric privacy and security for smart camera networks, Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC '10, p.85, 2010.
DOI : 10.1145/1865987.1866009

B. Wu and R. Nevatia, Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, ICCV, pp.90-97, 2005.

C. R. Huang, Y. T. Chen, C. S. Chen, and Y. P. Hung, Efficient hierarchical method for background subtraction, International Conference on Pattern Recognition, pp.2706-2715, 2007.

H. Yang, H. Su, S. Zheng, S. Wei, and Y. Fan, The large-scale crowd density estimation based on sparse spatiotemporal local binary pattern, IEEE International Conference on Multimedia and Expo, pp.1-6, 2011.

A. Yilmaz, O. Javed, and M. Shah, Object tracking, ACM Computing Surveys, vol.38, issue.4, 2006.
DOI : 10.1145/1177352.1177355

B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Velastin, and L. Xu, Crowd analysis: a survey, Machine Vision and Applications, vol.26, issue.9, pp.5-6345, 2008.
DOI : 10.1007/s00138-008-0132-4

URL : http://eprints.kingston.ac.uk/8264/

Y. Zhang, L. Qiny, H. Yao, P. Xu, and Q. Huang, Beyond particle flow: Bag of Trajectory Graphs for dense crowd event recognition, 2013 IEEE International Conference on Image Processing, p.19, 2013.
DOI : 10.1109/ICIP.2013.6738737

T. Zhao, R. Nevatia, and B. Wu, Segmentation and Tracking of Multiple Humans in Crowded Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.7, pp.1198-1211, 2008.
DOI : 10.1109/TPAMI.2007.70770

X. Zhao, D. Gong, and G. Medioni, Tracking Using Motion Patterns for Very Crowded Scenes, ECCV, pp.315-328, 2012.
DOI : 10.1007/978-3-642-33709-3_23

Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.28-31, 2004.
DOI : 10.1109/ICPR.2004.1333992

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.4658

Z. Zivkovic and F. Van-der-heijden, Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, vol.27, issue.7, pp.773-780, 2006.
DOI : 10.1016/j.patrec.2005.11.005