. Livres, N. Benabbas, C. Ihaddadene, and . Djeraba, Motion pattern extraction and event detection for automatic visual surveillance Multi-Modal User Interactions in Controlled Environments, EURASIP Journal on Image and Video Processing, p.15, 2010.

. Dejraba, Event Detection in Crowd Scenes Using Statistical Models, chapter Advances in Knowledge Discovery and Management, p.133, 2010.

C. Internationales, [. Benabbas, and S. Amir, Adel Lablack et Chabane Djeraba Human action recognition using direction and magnitude models of motion, Internatioal Conference on Computer Vision and Applications (VISAPP), 2011 [BIYD10] Yassine Benabbas

E. L. Andrade, B. Scott, and R. B. Fisher, Hidden Markov Models for Optical Flow Analysis in Crowds, 18th International Conference on Pattern Recognition (ICPR'06), pp.460-463, 2006.
DOI : 10.1109/ICPR.2006.621

Q. [. Aggarwal and . Cai, Human motion analysis : a review, IEEE Workshop on Nonrigid and Articulated Motion, pp.90-102, 1997.

B. Antic, D. Letic, and D. , CRNOJEVIC : K-means based segmentation for real-time zenithal people counting, International Conference on Image Processing, 2009.
DOI : 10.1109/icip.2009.5414001

A. Albiol, I. Mora, and V. Naranjo, Real-time high density people counter using morphological tools, IEEE Conference on Intelligent Transportation Systems, 2001.
DOI : 10.1109/6979.969366

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6432

A. Catherine, Q. Xingtai, M. Arash, and M. Maurice, A novel approach for recognition of human actions with semi-global features, Machine Vision and Applications (MVA), pp.27-34, 2008.

A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.3, pp.555-560, 2008.
DOI : 10.1109/TPAMI.2007.70825

B. Yassine, A. Samir, L. Adel, and D. Chabane, Human action recognition using direction and magnitude models of motion, Internatioal Conference on Computer Vision and Applications (VISAPP), 2011.

J. [. Beauchemin and . Barron, The computation of optical flow, ACM Computing Surveys, vol.27, issue.3, pp.433-466, 1995.
DOI : 10.1145/212094.212141

B. Axel, B. Marco, E. Julia, K. Matthias, H. S. Loos et al., A review and comparison of measures for automatic video surveillance systems, EURASIP Journal on Image and Video Processing, 2008.

B. Massimiliano, C. Luigi, and S. Enver, A statistical method for people counting in crowded environments, 14th International Conference on Image Analysis and Processing (ICIAP), pp.506-511, 2007.

J. [. Bobick and . Davis, The recognition of human movement using temporal templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.3, pp.257-267, 2001.
DOI : 10.1109/34.910878

B. Thierry, F. El, B. Bertrand, and V. , Background Modeling using Mixture of Gaussians for Foreground Detection -A Survey, Recent Patents on Computer Science, vol.1, issue.11, pp.219-237, 2008.

B. Herbert, E. Andreas, T. Tinne, V. Luc, and . Gool, Speeded-up robust features (surf), Computer Vision Image Understanding (CVIU), vol.110, pp.346-359, 2008.

]. D. Bey00, BEYMER : Person counting using stereo, Workshop on Human Motion (HUMO), pp.127-133, 2000.

B. Moshe, G. Lena, S. Eli, I. Michal, and B. Ronen, Actions as space-time shapes, IEEE International Conference on Computer Vision (ICCV), pp.1395-1402, 2005.

A. [. Basharat, M. Gritai, and . Shah, Learning object motion patterns for anomaly detection and improved object detection, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587510

B. E. Boser, I. M. Guyon, and N. Vladimir, VAPNIK : A training algorithm for optimal margin classifiers, 5th Annual ACM Workshop on Computational Learning Theory, pp.144-152, 1992.

M. Et-ramini and S. , Vhcve : A collaborative virtual environment including facial animation and computer vision, Proceedings of the 2009 VIII Brazilian Symposium on Games and Digital Entertainment, SBGAMES '09, pp.207-213, 2009.

B. Yassine, I. Nacim, and D. Chabane, Global analysis of optical flow vectors for event detection in crowd scenes In International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), BID11] Yassine BENABBAS, Nacim IHADDADENE et Chabane DJERABA : Motion pattern extraction and event detection for automatic visual surveillance. EURASIP Journal on Image and Video Processing, p.15, 2009.

]. S. Bir96 and . Birchfield, Klt : An implementation of the kanade-lucas-tomasi feature tracker, 1996.

B. Yassine, I. Nacim, U. Thierry, and D. Chabane, Analyse globale du flux optique pour la détection d'évènements dans une scène de foule, Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC), pp.339-350, 2010.

B. Yassine, I. Nacim, Y. Tarek, and D. Chabane, Spatio-temporal optical flow analysis for people counting, International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2010.

B. Yassine, L. Adel, I. Nacim, and D. Chabane, Action recognition using direction models of motion, International Conference on Pattern Recognition (ICPR), pp.4295-4298, 2010.

B. Yassine, L. Adel, U. Thierry, and D. Chabane, Reconnaissance d'actions par mod'elisation du mouvement, 11ème Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC), 2011.

I. [. Baker and . Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-225, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

B. [. Barandiaran and F. Murguia, BOTO : Real-time people counting using multiple lines, IEEE Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008.

A. B. Chan, M. Mulloy, and V. Nuno, Analysis of crowded scenes using holistic properties, 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009.

F. Aaron, BOBICK : Movement, activity and action : The role of knowledge in the perception of motion, Philosophical Transactions : Biological Sciences, vol.352, pp.1257-1265, 1358.

S. [. Boghossian, VELASTIN : Motion-based machine vision techniques for the management of large crowds, Proceedings of ICECS '99. The 6th IEEE International Conference on Electronics, Circuits and Systems, pp.961-964, 1999.

B. Yassine, Y. Tarek, U. Thierry, and D. Chabane, Analyse spatiotemporelle des vecteurs de mouvement : application au comptage des personnes, Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC), pp.173-178, 2011.

A. [. Cupillard, F. Avanzi, and . Bremond, THONNAT : Video understanding for metro surveillance, IEEE International Conference on Networking, Sensing and Control, pp.186-191, 2004.
DOI : 10.1109/icnsc.2004.1297432

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.8484

C. Yang, G. Haifeng, Z. Song-chun, and T. Yandong, Flow mosaicking: Real-time pedestrian counting without scene-specific learning, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1093-1100, 2009.
DOI : 10.1109/CVPR.2009.5206648

C. [. Cheung and . Kamath, Robust Background Subtraction with Foreground Validation for Urban Traffic Video, Special Issue on Advances in Intelligent Vision Systems : Methods and Applications, pp.2330-2340, 2005.
DOI : 10.1155/ASP.2005.2330

C. Chao, A. Liaw, and B. Leo, Using Random Forest to Learn Imbalanced Data, 2004.

R. T. Collins, A. J. Lipton, and K. Takeo, Introduction to the special section on video surveillance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.22745-746, 2000.
DOI : 10.1109/TPAMI.2000.868676

D. Chaabane, L. Adel, and B. Yassine, Multi-Modal User Interactions in Controlled Environments, 2010.

N. [. Dempster and D. B. Laird, RUBIN : Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

]. P. Drc-+-05, V. Dollar, G. Rabaud, E. S. Cottrell, and . Belongie, Behavior recognition via sparse spatio-temporal features, International Workshop on Visual Sur- Bibliographie veillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), 2005.

P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, Behavior Recognition via Sparse Spatio-Temporal Features, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp.65-72, 2005.
DOI : 10.1109/VSPETS.2005.1570899

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

F. El, B. Thierry, B. Bertrand, and V. , Comparison of background subtraction methods for a multimedia learning space, International Conference on Signal Processing and Multimedia (SIGMAP), 2007.

K. [. Elhabian, S. El-sayed, and . Ahmed, Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art, Recent Patents on Computer Science, vol.1, issue.1, pp.32-54, 2008.
DOI : 10.2174/1874479610801010032

E. Markus, M. Dariu, and . Gavrila, Monocular pedestrian detection : Survey and experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), issue.12, pp.312179-2195, 2009.

A. David, . Forsyth, A. Okan, I. Leslie, O. James et al., Computational studies of human motion : part 1, tracking and motion synthesis. Found. Trends, Comput. Graph. Vis, vol.1, issue.2-3, pp.77-254, 2005.

S. [. Friedman and . Russell, Image segmentation in video sequences : A probabilistic approach, 13th Conference on Uncertainty in Artificial Intelligence (UAI), pp.175-181, 1997.

F. Yoav and E. Robert, SCHAPIRE : A decision-theoretic generalization of on-line learning and an application to boosting, European Conference on Computational Learning Theory, pp.23-37, 1995.

M. Dariu and . Gavrila, The visual analysis of human movement : a survey, Computer Vision and Image Understanding (CVIU), vol.1, issue.73, pp.82-92, 1999.

L. Gary, J. E. Gaile, and . Burt, Directional Statistics. Concepts and techniques in modern geography, 1980.

]. A. Gbj-+-07, I. Gardel, P. Bravo, J. L. Jimenez, and . Lazaro, TORQUEMADA : Real time head detection for embedded vision modules, IEEE International Symposium on Intelligent Signal Processing (WISP), pp.1-6, 2007.

G. Lena, B. Moshe, S. Eli, I. Michal, and B. Ronen, Actions as space-time shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), issue.12, pp.292247-2253, 2007.

G. Garcia-bunster and M. Torres-torriti, Effective Pedestrian Detection and Counting at Bus Stops, 2008 IEEE Latin American Robotic Symposium, pp.158-163, 2008.
DOI : 10.1109/LARS.2008.18

G. Lynne, C. Avinash, and . Kak, Interactive learning of a multiple-attribute hash table classifier for fast object recognition, Computer Vision and Image Understanding, issue.3, pp.61387-416, 1995.

G. David, A. M. Lopez, A. D. Sappa, and G. Thorsten, Survey of pedestrian detection for advanced driver assistance systems, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.32, issue.7, pp.1239-1258, 2010.

M. [. Gandhi and . Trivedi, Pedestrian Protection Systems: Issues, Survey, and Challenges, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.3, pp.413-430, 2007.
DOI : 10.1109/TITS.2007.903444

J. M. Gryn, R. P. Wildes, and K. John, Detecting motion patterns via direction maps with application to surveillance, Computer Vision and Image Understanding, vol.113, issue.2, pp.291-307, 2009.
DOI : 10.1016/j.cviu.2008.10.006

H. Min, A. Saad, and S. Mubarak, Detecting global motion patterns in complex videos, ICPR'08 : International Conference on Pattern Recognition, 2008.

H. Min, A. Saad, and S. Mubarak, Learning motion patterns in crowded scenes using motion flow field, International Conference on Pattern Recognition (ICPR), 2008.

K. P. Hs81a-]-berthold, . Horn, and G. Brian, SCHUNCK : Determining optical flow, ARTIFI- CAL INTELLIGENCE, vol.17, pp.185-203, 1981.

]. B. Hs81b and B. G. Horn, SCHUNK : Determining optical flow, Artificial Intelligence, vol.17, pp.185-203, 1981.

H. Weiming, T. Tieniu, W. Liang, and S. Maybank, A survey on visual surveillance of object motion and behaviors, IEEE Transactions on Systems, Man, and Cybernetics, Part C : Applications and Reviews, vol.34, pp.334-352, 2004.

H. Weiming, X. Xuejuan, F. Zhouyu, X. Dan, T. Tieniu et al., A system for learning statistical motion patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.9, pp.1450-1464, 2006.
DOI : 10.1109/TPAMI.2006.176

I. Nacim and D. Chabane, Real-time crowd motion analysis

J. Gunnar, S. Sture, B. William, E. Gunnar, and J. , Perceiving Events and Objects, 1994.

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

V. Krüger, D. Kragic, A. Ude, C. Michael, K. Andreas et al., The meaning of action : A review on action recognition and mapping Using transduction and multi-view learning to answer emails, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp.1473-1501, 2003.

K. Alexander, M. Marcin, and S. Cordelia, A spatiotemporal descriptor based on 3d-gradients, British Machine Vision Conference (BMVC), pp.995-1004, 2008.

K. [. Kratz and . Nishino, Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1446-1453, 2009.
DOI : 10.1109/CVPR.2009.5206771

L. Sheng-fuu, C. Jaw-yeh, and C. Hung-xin, Estimation of number of people in crowded scenes using perspective transformation, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.31, issue.6, pp.31645-654, 2001.
DOI : 10.1109/3468.983420

L. Ivan, C. Barbara, S. Christian, and L. Tony, Local velocity-adapted motion events for spatio-temporal recognition, Computer Vision and Image Understanding (CVIU), vol.108, pp.207-229, 2007.

W. [. Lin, J. Grimson, and . Fisher, Learning visual flows : A lie algebraic approach, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.747-754, 2009.

M. [. Lee and . Hedley, Background estimation for video surveillance, Image and Vision Computing New Zealand (IVCNZ), pp.315-320, 2002.

T. [. Lucas and . Kanade, An iterative image registration technique with an application to stereo vision, International Joint Conference on Artificial Intelligence (IJCAI), pp.674-679, 1981.

L. Ivan and L. Tony, Space-time interest points, International Conference on Computer Vision (ICCV), 2003.

T. [. Laptev and . Lindeberg, Velocity adaptation of space-time interest points, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.52-56, 2004.
DOI : 10.1109/ICPR.2004.1334003

L. Wei-lwun and J. J. , LITTLE : Simultaneous tracking and action recognition using the pca-hog descriptor, The 3rd Canadian Conference on Computer and Robot Vision, p.6, 2006.

L. Ivan, M. Marcin, C. Schmid, and R. Benjamin, Learning realistic human actions from movies, International Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

G. David and . Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision (IJCV), vol.60, pp.91-110, 2004.

L. Xiaowei, T. Shasha, J. Jiafu, S. Jing, Q. Zhihong et al., Moving human head detection for automatic passenger counting system, éditeurs : Recent Advances in Computer Science and Information Engineering, pp.147-152, 2012.

]. X. Ltr-+-05, P. H. Liu, J. Tu, A. Rittscher, N. Perera et al., Detecting and counting people in surveillance applications, IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, pp.306-311, 2005.

L. Adel, U. Thierry, B. Yassine, and D. Chabane, Extraction de la région d'intérêt d'une personne sur un obstacle, Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC), pp.683-684, 2010.

P. [. Levin, Y. Viola, and . Freund, Unsupervised improvement of visual detectors using co-training, International Conference on Computer Vision (ICCV), volume I, pp.626-633, 2003.

L. Jingen, Y. Yang, S. Imran, and S. Mubarak, Learning semantic features for action recognition via diffusion maps, Computer Vision and Image Understanding, vol.116, issue.3, pp.361-377, 2012.

T. B. Moeslund, H. Adrian, and K. Volker, A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, vol.104, issue.2-3, pp.90-126, 2006.
DOI : 10.1016/j.cviu.2006.08.002

J. Stephen, . Mckenna, J. Sumer, D. Zoran, R. Azriel et al., Tracking groups of people, Computer Vision and Image Understanding, vol.80, issue.1, pp.42-56, 2000.

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

S. Messelodi, C. Modena, N. Segata, and M. Zanin, A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes, 13th International Conference on Image Analysis and Processing (ICIAP), pp.163-170, 2005.
DOI : 10.1007/11553595_20

M. Ramin, A. Oyama, and S. Mubarak, Abnormal crowd behavior detection using social force model, International Conference on Computer Vision and Pattern Recognition, 2009.

M. Ross, P. Chris, and K. Henry, Activity recognition using the velocity histories of tracked keypoints, International Conference on Computer Vision, 2009.

M. [. Morris and . Trivedi, A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance, IEEE Transactions on Circuits and Systems for Video Technology, pp.1114-1127, 2008.
DOI : 10.1109/TCSVT.2008.927109

]. J. Pet00 and . Petersen, Understanding Surveillance Technologies, 2000.

]. M. Pic04 and . Piccardi, Background subtraction techniques : A review The Hague, The Netherlands, International Conference on Systems, Man and Cybernetics (SMC), 2004.

M. [. Papageorgiou and . Oren, POGGIO : A general framework for object detection, International Conference on Computer Vision (ICCV), pp.555-562, 1998.

P. Ronald, A survey on vision-based human action recognition, Image and Vision Computing (IVC), vol.28, issue.6, pp.976-990, 2010.

]. F. Por03 and . Porikli, Human body tracking by adaptive background models and meanshiftanalysis, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2003.

O. [. Porikli and . Tuzel, Bayesian background modeling for foreground detection, Proceedings of the third ACM international workshop on Video surveillance & sensor networks , VSSN '05, pp.55-58, 2005.
DOI : 10.1145/1099396.1099407

A. [. Pande and . Verma, MITTAL : Network aware optimal resource allocation for e-learning videos, 6th International Conference on mobile Learning, 2007.

R. Mikel, A. Saad, and K. Takeo, Tracking in unstructured crowded scenes, International Conference on Computer Vision (ICCV), 2009.

A. Henry, . Rowley, B. Shumeet, and K. Takeo, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.20, pp.23-38, 1996.

R. Szeliski, Computer Vision Algorithms and Applications, 2011.

S. Mei-ling, Z. Xie-abd, M. Chen, and C. Shu-ching, Video semantic event/concept detection using a subspace-based multimedia datamining framework, IEEE transactions on multimedia, pp.252-259, 2008.

S. Paul, A. Saad, and S. Mubarak, A 3-dimensional sift descriptor and its application to action recognition, Proceedings of the 15th international conference on Multimedia, MULTIMEDIA '07, pp.357-360, 2007.

W. [. Stauffer and . Grimson, Adaptative background mixture models for real-time tracking, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.246-252, 1999.

S. Chris, W. Eric, and L. Grimson, Learning patterns of activity using realtime tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), issue.8, pp.22747-757, 2000.

Y. [. Sidla, N. Lypetskyy, and . Brandle, SEER : Pedestrian detection and tracking for counting applications in crowded situations, EEE International Conference on Video and Signal Based Surveillance, pp.70-75, 2006.

S. Christian, L. Ivan, and C. Barbara, Recognizing human actions : A local svm approach, International Conference on Pattern Recognition (ICPR), 2004.

S. Cordelia, M. Roger, and B. Christian, Evaluation of interest point detectors, International Journal of Computer Vision (IJCV), vol.37, issue.2, pp.151-172, 2000.

N. [. Sigari and . Mozayani, POURREZA : Fuzzy running average and fuzzy background subtraction : concepts and application, Int J Comput Sci Network Security, vol.8, issue.2, pp.138-143, 2008.

[. Delpozo, S. Savarese, J. Carlos, N. Li, and F. , Spatial-temporal correlations for unsupervised action classification, Proceedings of the Workshop on Applications of Computer Vision (WACV), 2008.

S. Jianbo and T. Carlo, Good features to track, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, pp.593-600, 1994.
DOI : 10.1109/CVPR.1994.323794

R. [. Turaga, V. S. Chellappa, and . Subrahmanian, UDREA : Machine recognition of human activities : A survey, IEEE Transactions on Circuits and Systems for Video Technology, pp.1473-1488, 2008.

T. Christian and H. Vaclav, Pose primitive based human action recognition in videos or still images, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008.

K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallflower: principles and practice of background maintenance, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.255-261, 1999.
DOI : 10.1109/ICCV.1999.791228

K. Terada, D. Yoshida, S. Oe, and J. Yamagushi, A counting method of the number of passing people using a stereo camera IEEE 25th Annual Conference of Industrial Electronics Society : Statistical filters for crowd image analysis, Performance Evaluation of Tracking and Surveillance workshop at CVPR 2009, pp.338-342, 1999.

V. Paul, J. Michael, and S. Daniel, Detecting pedestrians using patterns of motion and appearance, International Conference on Computer Vision (ICCV), pp.734-741, 2003.

. Vlth06, V. Senem, Y. Li, T. Arun, and H. , Automatic counting of interacting people by using a single uncalibrated camera, International Conference on Multimedia and Expo (ICME), pp.1265-1268, 2006.

T. Van, O. Sander, B. Ben, and J. A. Kröse, Head detection in stereo data for people counting and segmentation, Sixth International Conference on Computer Vision Theory and Applications (VISAPP), pp.620-625, 2011.

]. C. Wadp97a, A. Wren, T. Azarbayejani, and A. P. Darrell, PENTLAND : Pfinder : real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.780-785, 1997.

[. Richard, W. Ali, A. Trevor, D. Alex, and P. , Pfinder : Real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.19, issue.7, pp.780-785, 1997.

W. Liang, H. Weiming, and T. Tieniu, Recent developments in human motion analysis, Pattern Recognition, vol.36, issue.3, pp.585-601, 2003.

W. Shandong, B. E. Moore, and S. Mubarak, Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes, International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

D. [. Wang and . Suter, A Novel Robust Statistical Method for Background Initialization and Visual Surveillance, Asian Conference on Computer Vision (ACCV), pp.328-337, 2006.
DOI : 10.1007/11612032_34

W. Xiaogang, T. Kinh, and G. Eric, Learning semantic scene models by trajectory analysis, European Conference on Computer Vision (ECCV), 2006.

W. Geert, T. Tinne, and G. Luc, An efficient dense and scale-invariant spatio-temporal interest point detector, Proceedings of the 10th European Conference on Computer Vision : Part II, pp.650-663, 2008.

. Xiaowei, . Xu, W. Zhiyan, L. Yinghong, and Z. Yanqing, A rapid method for passing people counting in monocular video sequences, The Sixth International Conference on Machine Learning and Cybernetics, pp.1657-1662, 2007.

Y. Shengsheng, C. Xiaoping, S. Weiping, and X. Deping, A robust method for detecting and counting people, 2008 International Conference on Audio, Language and Image Processing, pp.1545-1549, 2008.
DOI : 10.1109/ICALIP.2008.4590257

D. B. Yang, H. H. Gonzalez-banos, and L. J. Guibas, Counting people in crowds with a real-time network of simple image sensors, Proceedings Ninth IEEE International Conference on Computer Vision, pp.122-129, 2003.
DOI : 10.1109/ICCV.2003.1238325

L. [. Yahiaoui and . Khoudour, A People Counting System Based on Dense and Close Stereovision, IEEE International Conference on Image and Signal processing, pp.59-66, 2008.
DOI : 10.1007/978-3-540-69905-7_7

URL : https://hal.archives-ouvertes.fr/hal-00521106

G. [. Yu and . Medioni, Motion pattern interpretation and detection for tracking moving vehicles in airborne video, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.2671-2678, 2009.

P. [. Jeon and . Rybski, Analysis of a spatio-temporal clustering algorithm for counting people in a meeting, 2006.

F. [. Zhang and . Chen, A Fast and Robust People Counting Method in Video Surveillance, 2007 International Conference on Computational Intelligence and Security (CIS 2007), pp.339-343, 2007.
DOI : 10.1109/CIS.2007.85

Z. Xi, D. Emmanuel, and C. Liming, A people counting system based on face detection and tracking in a video, International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp.67-72, 2009.

H. [. Zhang, S. Z. Lu, and . Li, Learning semantic scene models by object classification and trajectory clustering, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.1940-1947, 2009.

Z. Beibei, M. Dorothy, R. Paolo, V. Sergio, and X. Li-qun, Crowd analysis : a survey, Machine Vision and Applications, vol.19, pp.345-357, 2008.

Z. Tao and N. Ram, Bayesian human segmentation in crowded situations, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), p.459, 2003.

Z. Tao and N. Ram, Tracking multiple humans in complex situations

Z. Yanqing, W. Zhiyan, and W. Bin, A camera calibration method based on nonlinear model and improved planar pattern, JCIS/CVPRIP, vol.3, pp.707-7012, 2005.