S. Mann, Wearable computing: a first step toward personal imaging, Computer, vol.30, issue.2, pp.25-32, 1997.
DOI : 10.1109/2.566147

S. Hodges, L. Williams, E. Berry, S. Izadi, J. Srinivasan et al., SenseCam: A Retrospective Memory Aid, International Conference on Ubiquitous Computing, pp.177-193, 2006.
DOI : 10.1007/11853565_11

K. Perez, C. Helmer, H. Amieva, J. Orgogozo, I. Rouch et al., Natural history of decline in instrumental activities of daily living performance over the 10 years preceding the clinical diagnosis of dementia: a prospective population-based study, J Am Geriatr Soc, vol.56, issue.1, pp.37-44, 2008.

R. Megret, D. Szolgay, J. Benois-pineau, J. Ph, J. Pinquier et al., Wearable video monitoring of people with age Dementia : Video indexing at the service of helthcare, 2008 International Workshop on Content-Based Multimedia Indexing, pp.101-108, 2008.
DOI : 10.1109/CBMI.2008.4564934
URL : https://hal.archives-ouvertes.fr/hal-00349162

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

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

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

L. Carminati and J. Benois-pineau, Gaussian mixture classification for moving object detection in video surveillance environment, IEEE International Conference on Image Processing 2005, pp.113-116, 2005.
DOI : 10.1109/ICIP.2005.1530341
URL : https://hal.archives-ouvertes.fr/hal-00308319

M. Balcells-capellades, D. Dementhon, and D. Doermann, An appearance-based approach for consistent labeling of humans and objects in video, Pattern Analysis and Applications, vol.29, issue.1, pp.373-385, 2004.
DOI : 10.1007/s10044-004-0237-y

K. Kim, T. Chalidabhongse, D. Harwood, and L. Davis, Background modeling and subtraction by codebook construction, Proceedings of IEEE International Conference of Image Processing, pp.3061-3064, 2004.

A. Mittal and N. Paragios, Motion-based background subtraction using adaptive kernel density estimation, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.302-309, 2004.
DOI : 10.1109/CVPR.2004.1315179

T. Tiand, C. Tomasi, and D. Heeger, Comparision of approaches to ego-motion computation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.315-320, 1996.

G. Foresti and C. Micheloni, A robust feature tracker for active surveillance of outdoor scenes, Electron Lett Comput Vis Image Anal, vol.1, issue.1, pp.21-34, 2003.

B. Jung and G. Sukhatme, Detecting moving objects using a single camera on a mobile robot in an outdoor environment, the Conference on Intelligent Autonomous Systems, pp.980-987, 2004.

A. Jung-ho, C. Cheolmin, K. Sooyeong, K. Kilcheon, and B. Hyeran, Human tracking and silhouette extraction for human? robot interaction systems, Pattern Anal Appl, 2008.

T. Veit, F. Cao, and . Bouthemy, An a contrario Decision Framework for Region-Based Motion Detection, International Journal of Computer Vision, vol.17, issue.10, pp.163-178, 2006.
DOI : 10.1007/s11263-006-6661-2

C. Yuan, G. Medioni, J. Kang, and I. Cohen, Detecting Motion Regions in the Presence of a Strong Parallax from a Moving Camera by Multiview Geometric Constraints, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.9, pp.1627-1641, 2007.
DOI : 10.1109/TPAMI.2007.1084

Y. Sheikh and M. Shah, Bayesian modeling of dynamic scenes for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.11, pp.1778-1792, 2005.
DOI : 10.1109/TPAMI.2005.213

M. Accame, F. De-natale, and D. Giusto, High Performance Hierarchical Block-based Motion Estimation for Real-Time Video Coding, Real-Time Imaging, vol.4, issue.1, pp.67-79, 1998.
DOI : 10.1006/rtim.1996.0064

M. Durik and J. Benois-pineau, Robust motion characterisation for video indexing based on MPEG2 opticalflow, Proceedings of the International Workshop on Content-Based Multimedia Indexing, pp.57-64, 2001.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472

C. Archambeau, M. Valle, A. Assenza, and M. Verleysen, Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures, 2006 IEEE International Symposium on Circuits and Systems, pp.1-4, 2006.
DOI : 10.1109/ISCAS.2006.1693317

R. Duda, P. Hart, and D. Stork, Pattern classification, pp.311-328, 2001.

A. Bugeau, Détection et suivi d'objets en mouvement dans des scènes complexes, application a ` la surveillance des conducteurs , Thèse de l'université de Rennes 1, Mention Traitement du Signal et des Télécommunications 27 Nonparametric estimates of a multivariate probability density, Epanechnikov Theor Probab Appl, vol.14, pp.153-158, 1969.

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

J. Sander, M. Ester, H. Kriegel, and X. Xu, Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.169-194, 1998.
DOI : 10.1023/A:1009745219419

C. Van-rijsbergen, Information retrieval http://staff.science.uva.nl/*zivkovic/DOWNLOAD.html 32 Recursive unsupervised learning of finite mixture models, Zivkovic Z, van der Heijden F IEEE Trans Pattern Anal Mach Intell, vol.26, issue.5, pp.651-656, 1979.

G. Gupta and C. Chakrabarti, Architectures for hierarchical and other block matching algorithms, IEEE Transactions on Circuits and Systems for Video Technology, vol.5, issue.6, pp.477-489, 1995.
DOI : 10.1109/76.475890

S. Mazaré, R. Pacalet, and J. Dugelay, Using GPU for fast block-matching, 14th European Signal Processing Conference, 2006.

J. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger et al., A Survey of General-Purpose Computation on Graphics Hardware, Computer Graphics Forum, vol.7, issue.4, pp.80-113, 2007.
DOI : 10.1016/j.rti.2005.04.002