S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

F. Aurenhammer, Voronoi diagrams---a survey of a fundamental geometric data structure, ACM Computing Surveys, vol.23, issue.3, pp.345-405, 1991.
DOI : 10.1145/116873.116880

F. Aurenhammer and R. Klein, Voronoi diagrams, chapter, pp.201-290, 2000.

F. Bacao, V. Lobo, and M. Painho, Self-organizing maps as substitutes for k-means clustering, Proc. of the Int. Conf. on Computer Science, pp.476-483, 2005.

Y. Bar-shalom and T. E. Fortmann, Tracking and data association. Number 179 in Mathematics in science and engineering, 1988.

L. Baum, T. Petrie, G. Soules, and N. Weiss, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, pp.164-171, 1970.
DOI : 10.1214/aoms/1177697196

N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, The r*-tree: an efficient and robust access method for points and rectangles, Proc. of the 1990 ACM SIGMOD Int. Conf. on Management of data, pp.322-331, 1990.

R. Bellman, Adaptive control processes: a guided tour, 1961.
DOI : 10.1515/9781400874668

M. Bennewitz, W. Burgard, and S. Thrun, Learning motion patterns of persons for mobile service robots, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), pp.3601-3606, 2002.
DOI : 10.1109/ROBOT.2002.1014268

M. Bennewitz, W. Burgard, G. Cielniak, and S. Thrun, Learning Motion Patterns of People for Compliant Robot Motion, The International Journal of Robotics Research, vol.24, issue.1, pp.31-48, 2005.
DOI : 10.1177/0278364904048962

H. Binsztok and T. Artières, LEARNING MODEL STRUCTURE FROM DATA: AN APPLICATION TO ON-LINE HANDWRITING, Electronic Letter on Computer Vision and Image Analyisis, vol.5, issue.2, 2005.
DOI : 10.1142/9789812834461_0012

URL : https://hal.archives-ouvertes.fr/hal-01171008

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

M. Brand, Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction, Neural Computation, vol.37, issue.6, 1998.
DOI : 10.1002/jgt.3190010407

M. Brand and V. Kettnaker, Discovery and segmentation of activities in video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.844-851, 2000.
DOI : 10.1109/34.868685

M. Brand, N. Oliver, and A. Pentland, Coupled hidden Markov models for complex action recognition, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.994-999, 1997.
DOI : 10.1109/CVPR.1997.609450

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

A. Bruce and G. Gordon, Better motion prediction for people-tracking, Proc. of the IEEE Int. Conf. on Robotics and Automation, 2004.

H. Bui, S. Venkatesh, and G. West, Policy recognition in the abstract hidden markov models, Journal of Artificial Intelligence Research, vol.17, pp.451-499, 2002.

D. Buzan, S. Sclaroff, and G. Kollios, Extraction and clustering of motion trajectories in video, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004.
DOI : 10.1109/ICPR.2004.1334287

A. Caporossi, D. Hall, P. Reignier, and J. Crowley, Robust visual tracking from dynamic control of processing, International Workshop on Performance Evaluation of Tracking and Surveillance, pp.23-31, 2004.

A. R. Cassandra, L. P. Kaelbling, and J. A. Kurien, Acting under uncertainty: discrete bayesian models for mobile-robotnavigation, pp.963-972, 1996.

C. C. Chang and K. Song, Environment prediction for a mobile robot in a dynamic environment, IEEE Transactions on Robotics and Automation, vol.13, issue.6, pp.862-872, 1997.
DOI : 10.1109/70.650165

G. F. Cooper, The computational complexity of probabilistic inference using bayesian belief networks, Artificial Intelligence, vol.42, issue.2-3, pp.393-405, 1990.
DOI : 10.1016/0004-3702(90)90060-D

R. T. Cox, Probability, Frequency and Reasonable Expectation, American Journal of Physics, vol.14, issue.1, pp.1-13, 1946.
DOI : 10.1119/1.1990764

H. Dee and D. Hogg, Detecting inexplicable behaviour, Procedings of the British Machine Vision Conference 2004, pp.49-55, 2004.
DOI : 10.5244/C.18.50

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

H. Dee, Explaining Visible Behaviour, 2005.

N. Dempster, A. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.9, issue.1, pp.1-38, 1977.

D. C. Dennett, The Intentional Stance, 1987.

A. K. Dewdney and J. K. Vranch, A convex partition of r 3 with applications to crum's problem and knuth's post-office problem, Utilitas Math, vol.12, pp.193-199, 1977.

R. A. Dwyer, Higher-dimensional Voronoi diagrams in linear expected time, 1989.

A. Elnagar and K. K. Gupta, Motion prediction of moving objects based on autoregressive model, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.28, issue.6, pp.803-810, 1998.
DOI : 10.1109/3468.725351

D. Filliat, Cartographie et estimation globale de la position pour un robot mobile autonome, 2001.
URL : https://hal.archives-ouvertes.fr/tel-00655469

A. F. Foka and P. E. Trahanias, Predictive autonomous robot navigation, IEEE/RSJ International Conference on Intelligent Robots and System, pp.490-495, 2002.
DOI : 10.1109/IRDS.2002.1041438

G. D. Forney, The viterbi algorithm, Proceedings of the IEEE, pp.268-278, 1973.
DOI : 10.1109/PROC.1973.9030

D. Freitag and A. Mccallum, Information extraction with hmm structures learned by stochastic optimization, Proc. of the Seventeenth Nat. Conf. on Artificial Intelligence and Twelfth Conf. on Innovative Applications of Artificial Intelligence, pp.584-589, 2000.

N. Friedman, Learning belief networks in the presence of missing values and hidden variables, Proc. of the Fourteenth International Conference on Machine Learning, pp.125-133, 1997.

B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, 1995.

S. Gong and T. Xiang, Recognition of group activities using dynamic probabilistic networks, Proceedings of the Ninth IEEE International Conference on Computer Vision, pp.742-749, 2003.

A. Guttman, R-trees: a dynamic index structure for spatial searching, Proc. of the 1984 ACM SIGMOD Int. Conf. on Management of data, pp.47-57, 1984.

R. K. Guy, Unsolved Problems Come of Age, The American Mathematical Monthly, vol.96, issue.10, pp.903-909, 1989.
DOI : 10.2307/2324584

K. Han and M. Veloso, Physical model based multi-objects tracking and prediction in robosoccer, Working notes of the AAAI 1997 Fall Symposium on Model-directed Autonomous Systems, 1997.

D. Heckerman, A tutorial on learning with bayesian networks, 1995.

R. A. Howard, Dynamic Programming and Markov Process, 1960.

W. Hu, D. Xie, and T. Tan, A Hierarchical Self-Organizing Approach for Learning the Patterns of Motion Trajectories, IEEE Transactions on Neural Networks, vol.15, issue.1, pp.135-144, 2004.
DOI : 10.1109/TNN.2003.820668

W. Hu, D. Xie, T. Tieniu, and S. Maybank, Learning Activity Patterns Using Fuzzy Self-Organizing Neural Network, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.3, pp.1618-1626, 2004.
DOI : 10.1109/TSMCB.2004.826829

W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan 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.

C. Igel, T. Suttorp, and N. Hansen, A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.453-460, 2006.
DOI : 10.1145/1143997.1144082

T. Irony and N. Singpurwalla, Non-informative priors do not exist A dialogue with Jos?? M. Bernardo, Journal of Statistical Planning and Inference, vol.65, issue.1, pp.159-189, 1997.
DOI : 10.1016/S0378-3758(97)00074-8

A. H. Rittscher, A. Blake, and G. Stein, Mathematical modelling of animate and intentional motion, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.358, issue.1431, pp.475-490, 1431.
DOI : 10.1098/rstb.2002.1259

A. Jain, M. Murty, and P. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.265-322, 1999.
DOI : 10.1145/331499.331504

E. T. Jaynes, Probability theory-the logic of science. Unpublished manuscript, 1995.

J. Jockusch and H. Ritter, An instantaneous topological map for correlated stimuli, Proc. of the International Joint Conference on Neural Networks, pp.529-534, 1999.

N. Johnson and D. Hogg, Learning the distribution of object trajectories for event recognition, Proc. of the British Machine Vision Conference, pp.583-592, 1995.
DOI : 10.1016/0262-8856(96)01101-8

B. Juang, S. E. Levinson, and M. M. Sondhi, Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains, AT&T Technical Journal, vol.64, issue.6, pp.307-309, 1986.
DOI : 10.1002/j.1538-7305.1985.tb00273.x

S. Julier and J. Uhlmann, New extension of the Kalman filter to nonlinear systems, Signal Processing, Sensor Fusion, and Target Recognition VI, 1997.
DOI : 10.1117/12.280797

I. Junejo, O. Javed, and M. Shah, Multi feature path modeling for video surveillance, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.716-719, 2004.
DOI : 10.1109/ICPR.2004.1334359

R. 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

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series In Probability And Mathematical Statistics, 1989.
DOI : 10.1002/9780470316801

B. King, Step-Wise Clustering Procedures, Journal of the American Statistical Association, vol.3, issue.317, pp.86-101, 1967.
DOI : 10.1080/01621459.1963.10500845

T. Kohonen, The 'neural' phonetic typewriter, Computer, vol.21, issue.3, pp.11-22, 1988.
DOI : 10.1109/2.28

T. Kohonen, Self-Organizing Maps, volume 30 of Springer Series in Information Sciences, 1995.

E. B. Koller-meier and L. Van-gool, Modeling and Recognition of Human Actions Using a Stochastic Approach, 2nd European Workshop on Advanced Video-Surveillance Systems, 2001.
DOI : 10.1007/978-1-4615-0913-4_15

B. J. Krose and M. Eecen, A self-organizing representation of sensor space for mobile robot navigation, Proc. of the IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems, pp.9-14, 1994.

E. Kruse and F. Wahl, Camera-based observation of obstacle motions to derive statistical data for mobile robot motion planning, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), pp.662-667, 1998.
DOI : 10.1109/ROBOT.1998.677048

E. Kruse, R. Gutsche, and F. Wahl, Estimation of collision probabilities in dynamic environments for path planning with minimum collision probability, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96, pp.1288-1295, 1996.
DOI : 10.1109/IROS.1996.568983

E. Kruse, R. Gusche, and F. M. Wahl, Acquisition of statistical motion patterns in dynamic environments and their application to mobile robot motion planning, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, pp.713-717, 1997.
DOI : 10.1109/IROS.1997.655089

B. Kuipers, A Hierarchy of Qualitative Representations for Space, Lecture Notes in Computer Science, vol.1404, pp.337-350, 1998.
DOI : 10.1007/3-540-69342-4_16

P. Langley, Learning in Humans and Machines: Towards an Interdisciplinary Learning Science, chapter Order effects in incremental learning, 1995.

S. E. Levinson, L. Rabiner, and M. M. Sondhi, An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition, Bell System Technical Journal, vol.62, issue.4, pp.1035-1074, 1983.
DOI : 10.1002/j.1538-7305.1983.tb03114.x

Y. Li, L. Xu, J. Morphett, and R. Jacobs, An integrated algorithm of incremental and robust PCA, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), pp.245-248, 2003.
DOI : 10.1109/ICIP.2003.1246944

L. Liao, D. Fox, J. Hightower, H. Kautz, and D. Schulz, Voronoi tracking: location estimation using sparse and noisy sensor data, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), 2003.
DOI : 10.1109/IROS.2003.1250715

Y. Linde, A. Buzo, and R. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, vol.28, issue.1, pp.84-95, 1980.
DOI : 10.1109/TCOM.1980.1094577

P. X. Liu, M. Meng, and C. Hu, On-line data-driven fuzzy clustering with applications to realtime robotic tracking, IEEE Int. Conf. on Robotics and Automation, pp.5039-5044, 2004.

S. P. Lloyd, Least squares quantization in pcm's. Bell Telephone Laboratories Paper, 1957.

P. Lockwood and M. Blanchet, An algorithm for the dynamic inference of hidden Markov models (DIHMM), IEEE International Conference on Acoustics Speech and Signal Processing, 1993.
DOI : 10.1109/ICASSP.1993.319282

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

D. Magee, Tracking multiple vehicles using foreground, background and motion models, Image and Vision Computing, vol.22, issue.2, pp.143-155, 2004.
DOI : 10.1016/S0262-8856(03)00145-8

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

D. Makris and T. Ellis, Finding Paths in Video Sequences, Procedings of the British Machine Vision Conference 2001, pp.263-272, 2001.
DOI : 10.5244/C.15.28

D. Makris and T. Ellis, Spatial and probabilistic modelling of pedestrian behavior, Proc. of the British Machine Vision Conference, pp.557-566, 2002.

T. Mann, Numerically stable hidden markov model implementation. An HMM scaling tutorial, 2006.

C. D. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, 1999.

S. Marsland, J. Shapiro, and U. Nehmzow, A self-organizing network that grows when required, Neural Networks, 2002.

T. Martinetz and K. Schulten, A " neural-gas " network learns topologies, Artificial Neural Networks, pp.397-402, 1991.

M. Meila, M. I. Jordan, and L. P. Kaelbling, Learning with mixtures of trees, Journal of Machine Learning Research, vol.1, issue.1, pp.1-48, 2001.

T. P. Minka, From hidden markov models to linear dynamical systems, 1999.

D. C. Minnen and C. R. Wren, Finding temporal patterns by data decomposition, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., pp.608-613, 2004.
DOI : 10.1109/AFGR.2004.1301600

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

K. P. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, 2002.

R. M. Neal and G. E. Hinton, A new view of the em algorithm that justifies incremental, sparse and other variants, Learning in Graphical Models, pp.355-368, 1998.

N. M. Oliver, B. Rosario, and A. P. Pentland, A Bayesian computer vision system for modeling human interactions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.831-843, 2000.
DOI : 10.1109/34.868684

S. Osentoski, V. Manfredi, and S. Mahadevan, Learning hierarchical models of activity, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004.
DOI : 10.1109/IROS.2004.1389465

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988.

A. Pentland and A. Liu, Modeling and Prediction of Human Behavior, Neural Computation, vol.83, issue.1, pp.229-242, 1999.
DOI : 10.1109/3468.553220

L. R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition . Readings in speech recognition, pp.267-296, 1990.

R. P. Rao, Robust kalman filters for prediction, recognition, and learn- ing, 1996.

J. Reif and M. Sharir, Motion planning in the presence of moving obstacles, Symp. on the Foundations of Computer Science, pp.144-154, 1985.

R. Madhavan and C. Schlenoff, Moving object prediction for off-road autonomous navigation, Proceedings of the SPIE Aerosense Conference, 2003.
DOI : 10.1117/12.485771

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

R. Rosales and S. Sclaroff, Improved tracking of multiple humans with trajectory prediction and occlusion modeling, IEEE CVPR Workshop on the Interpretation of Visual Motion, 1998.

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

K. Seymore, A. Mccallum, and R. Rosenfeld, Learning hidden markov model structure for information extraction, AAAI 99 Workshop on Machine Learning for Information Extraction, 1999.

Y. Singer and M. K. Warmuth, Training algorithms for hidden markov models using entropy based distance functions, Advances in Neural Information Processing Systems 9, NIPS, pp.641-647, 1996.

C. Stauffer and E. Grimson, Learning patterns of activity using real-time tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.747-757, 2000.
DOI : 10.1109/34.868677

A. Stolcke and S. Omohundro, Hidden markov model induction by bayesian model merging, Advances in Neural Information Processing Systems, pp.11-18, 1993.

A. Stolcke and S. M. Omohundro, Best-first model merging fr hidden markov model induction, 19941994.

L. Stone, C. Barlow, and T. Corwin, Bayesian Multiple Target Tracking, 1999.

N. Sumpter and A. Bulpitt, Learning spatio-temporal patterns for predicting object behaviour, Image and Vision Computing, vol.18, issue.9, pp.697-704, 2000.
DOI : 10.1016/S0262-8856(99)00073-6

S. Tadokoro, M. Hayashi, Y. Manabe, Y. Nakami, and T. Takamori, Motion planner of mobile robots which avoid moving human obstacles on the basis of stochastic prediction, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, pp.3286-3291, 1995.
DOI : 10.1109/ICSMC.1995.538292

R. C. Vasko, A. El-jaroudi, J. Boston, and T. E. Rudy, Hidden Markov model topology estimation to characterize the dynamic structure of repetitive lifting data, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136), pp.1725-1728, 1997.
DOI : 10.1109/IEMBS.1997.757055

D. Vasquez and T. Fraichard, Motion prediction for moving objects: a statistical approach, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, pp.3931-3936, 2004.
DOI : 10.1109/ROBOT.2004.1308883

URL : https://hal.archives-ouvertes.fr/inria-00182066

A. J. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, vol.13, issue.2, pp.260-269, 1967.
DOI : 10.1109/TIT.1967.1054010

M. Walter, A. Psarrow, and S. Gong, Learning prior and observation augmented density models for behaviour recognition, Proc. of the British Machine Vision Conference, pp.23-32, 1999.

X. Wang, K. Tieu, and E. Grimson, Learning Semantic Scene Models by Trajectory Analysis, 2006.
DOI : 10.1007/11744078_9

T. Xiang and S. Gong, Beyond Tracking: Modelling Activity and Understanding Behaviour, International Journal of Computer Vision, vol.22, issue.8, 2006.
DOI : 10.1007/s11263-006-4329-6

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

H. Yu and T. Su, Destination driven motion planning via obstacle motion prediction and multistate path repair, Journal of Intelligent and Robotic Systems, vol.36, issue.2, pp.149-173, 2003.
DOI : 10.1023/A:1022668100590

Z. Zhang, A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.11, pp.1330-1334, 2000.
DOI : 10.1109/34.888718

Q. Zhu, A stochastic algorithm for obstacle motion prediction in visual guidance of robot motion, IEEE International Conference on Systems Engineering, pp.216-219, 1990.
DOI : 10.1109/ICSYSE.1990.203136