J. M. Pérez-rúa, T. Crivelli, and P. Pérez, Backgroundforeground tracking for video object segmentation, Image Processing (ICIP), 2015 IEEE International Conference on, pp.1613-1617, 2015.

J. M. Pérez-rúa, T. Crivelli, and P. Pérez, Object-guided motion estimation. Computer Vision and Image Understanding, vol.153, pp.88-99, 2016.

J. M. Pérez-rúa, T. Crivelli, P. Pérez, and P. Bouthemy, Hierarchical motion decomposition for dynamic scene parsing, IEEE International Conference on, pp.3952-3956, 2016.

J. M. Pérez-rúa, T. Crivelli, P. Pérez, and P. Bouthemy, Discovering motion hierarchies via tree-structured coding of trajectories, 27th British Machine Vision Conference (BMVC 2016), vol.12, pp.106-107, 2016.

J. M. Pérez-rúa, T. Crivelli, P. Bouthemy, and P. Pérez, Determining occlusions from space and time image reconstructions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1382-1391, 2016.

O. Miksik, J. M. Pérez-rúa, P. H. Torr, and P. Pérez, ROAM: a Rich Object Appearance Model with Application to Rotoscoping, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1382-1391, 2017.
DOI : 10.1109/cvpr.2017.785

URL : https://hal.archives-ouvertes.fr/hal-01576017

J. M. Pérez-rúa, T. Crivelli, P. Bouthemy, and P. Pérez, Trimap tracking: closing the gap between rotoscoping and alpha matting, 2017.

J. M. Pérez-rúa, T. Crivelli, P. Bouthemy, and P. Pérez, Learning how to be robust: Deep polynomial regression, 2018.

. Ya?-giz-aksoy, M. Tunç-ozan-aydin, A. Pollefeys, and . Smoli´csmoli´c, Interactive high-quality green-screen keying via color unmixing, Transactions on Graphics, vol.35, issue.5, p.152, 2016.

J. Ahn and H. Byun, Accurate foreground extraction using graph cut with trimap estimation, Advances in Image and Video Technology: First Pacific Rim Symposium, PSIVT, 2006.
DOI : 10.1007/11949534_120

P. Allain, N. Courty, and T. Corpetti, Crowd flow characterization with optimal control theory, Asian Conference on Computer Vision, pp.279-290, 2009.
DOI : 10.1007/978-3-642-12304-7_27

URL : https://hal.archives-ouvertes.fr/hal-00493443

P. Allain, N. Courty, and T. Corpetti, Agoraset: a dataset for crowd video analysis, International Conference on Pattern Recognition, pp.1-6, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00904216

L. Alvarez, R. Deriche, T. Papadopoulo, and J. Sánchez, Symmetrical dense optical flow estimation with occlusions detection, International Journal of Computer Vision, vol.75, issue.3, pp.371-385, 2007.
DOI : 10.1007/s11263-007-0041-4

URL : https://hal.archives-ouvertes.fr/inria-00427151

M. Aharon, M. Elad, A. Bruckstein, and .. , An algorithm for designing overcomplete dictionaries for sparse representation, Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.

N. Apostoloff and A. Fitzgibbon, Learning spatiotemporal tjunctions for occlusion detection, Computer Vision Pattern Recognition, 2005.
DOI : 10.1109/cvpr.2005.206

A. Agarwala, A. Hertzmann, H. David, S. Salesin, and . Seitz, Keyframe-based tracking for rotoscoping and animation, Transactions on Graphics, vol.23, issue.3, pp.584-591, 2004.
DOI : 10.1145/1015706.1015764

URL : http://grail.cs.washington.edu/projects/rotoscoping/roto.pdf

P. Anandan, A computational framework and an algorithm for the measurement of visual motion, International Journal of Computer Vision, vol.2, issue.3, pp.283-310, 1989.

J. Christopher, B. L. Armstrong, W. Price, and . Barrett, Interactive segmentation of image volumes with live surface, Computers & Graphics, vol.31, issue.2, pp.212-229, 2007.

A. Ayvaci, M. Raptis, and S. Soatto, Sparse occlusion detection with optical flow, International Journal of Computer Vision, vol.97, issue.3, pp.322-338, 2012.
DOI : 10.1007/s11263-011-0490-7

URL : http://www.vision.cs.ucla.edu/papers/ayvaciRS10IJCV.pdf

S. Ali and M. Shah, Human action recognition in videos using kinematic features and multiple instance learning, Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.2, pp.288-303, 2010.
DOI : 10.1109/tpami.2008.284

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua et al., SLIC superpixels compared to state-of-the-art superpixel methods, Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.

A. Amir, T. E. Amini, R. C. Weymouth, and . Jain, Using dynamic programming for solving variational problems in vision, Transactions on Pattern Analysis and Machine Intelligence, 1990.

J. Michael, P. Black, and . Anandan, A framework for the robust estimation of optical flow, International Conference on Computer Vision, pp.231-236, 1993.

J. Michael, P. Black, and . Anandan, The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields, Computer Vision and Image Understanding, vol.63, issue.1, pp.75-104, 1996.

J. Bergen, P. Anandan, K. Hanna, and R. Hingorani, Hierarchical model-based motion estimation, European Conference on Computer Vision, pp.237-252, 1992.

R. Ben, -. , and N. Sochen, Variational stereo vision with sharp discontinuities and occlusion handling, International Conference on Computer Vision, 2007.

D. Myron-z-brown, G. Burschka, and . Hager, Advances in computational stereo, Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.8, pp.993-1008, 2003.

T. Brox, C. Bregler, and J. Malik, Large displacement optical flow, Computer Vision Pattern Recognition, pp.41-48, 2009.

B. Horace, C. Barlow, J. Blakemore, and . Pettigrew, The neural mechanism of binocular depth discrimination, The Journal of Physiology, vol.193, issue.2, p.327, 1967.

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High accuracy optical flow estimation based on a theory for warping, European Conference on Computer Vision, pp.25-36, 2004.

P. Bouthemy, M. Gelgon, and F. Ganansia, A unified approach to shot change detection and camera motion characterization. Transactions on Circuits and Systems for Video Technology, vol.9, pp.1030-1044, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00450210

C. Ballester and L. Garrido, Vanel Lazcano, and Vicent Caselles. A TV-L1 optical flow method with occlusion detection, Pattern Recognition, vol.7476, pp.31-40, 2012.

A. Blake and M. Isard, Active contours, 2000.

J. Michael, A. Black, and . Jepson, Estimating optical flow in segmented images using variable-order parametric models with local deformations, Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.10, pp.972-986, 1996.

J. Michael, A. Black, and . Jepson, Eigentracking: Robust matching and tracking of articulated objects using a view-based representation, International Journal of Computer Vision, vol.26, issue.1, pp.63-84, 1998.

Y. Yuri, M. Boykov, and . Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in nd images, International Conference on Computer Vision, vol.1, pp.105-112, 2001.

G. Baugh and A. Kokaram, Semi-automatic motion based segmentation using long term motion trajectories, International Conference on Image Processing, 2010.

S. Baker and I. Matthews, Lucas-kanade 20 years on: A unifying framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004.

S. Benhimane and E. Malis, Real-time image-based tracking of planes using efficient second-order minimization, International Conference on Intelligent Robots and Systems, vol.1, pp.943-948, 2004.

T. Brox and J. Malik, Object segmentation by long term analysis of point trajectories, European Conference on Computer Vision, 2010.

T. Brox and J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.500-513, 2011.

F. L. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.6, pp.567-585, 1989.

J. Bouguet, Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm, Intel Corporation, vol.5, pp.1-10, 2001.

T. Stanley, . Birchfield, . Shrinivas, and . Pundlik, Joint tracking of features and edges, Computer Vision Pattern Recognition, pp.1-6, 2008.

B. Bratt, Rotoscoping: Techniques and tools for the Aspiring Artist, 2011.

X. Bai and G. Sapiro, A geodesic framework for fast interactive image and video segmentation and matting, International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/iccv.2007.4408931

C. Barnes, E. Shechtman, A. Finkelstein, and D. Goldman, Patchmatch: a randomized correspondence algorithm for structural image editing, Transactions on Graphics, issue.3, p.24, 2009.
DOI : 10.1145/2018396.2018421

URL : http://www.connellybarnes.com/work/publications/2011_patchmatch_cacm.pdf

S. Baker, D. Scharstein, S. Lewis, . Roth, J. Michael et al., A database and evaluation methodology for optical flow, International Journal of Computer Vision, vol.92, issue.1, pp.1-31, 2011.

L. Burke, On the tunnel effect, Quarterly Journal of Experimental Psychology, vol.4, issue.3, pp.121-138, 1952.

O. Barnich and M. Van-droogenbroeck, Vibe: A universal background subtraction algorithm for video sequences, Transactions on Image Processing, vol.20, p.1709, 2011.

C. Bailer, K. Varanasi, and D. Stricker, Cnn-based patch matching for optical flow with thresholded hinge embedding loss, Computer Vision Pattern Recognition, 2017.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

URL : http://www.csd.uwo.ca/~yuri/Papers/iccv99.pdf

X. Bai, J. Wang, and D. Simons, Towards temporally-coherent video matting, MIRAGE, vol.11, pp.63-74, 2011.

D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, A naturalistic open source movie for optical flow evaluation, European Conference on Computer Vision, 2012.

J. Daniel, J. Butler, . Wulff, B. Garrett, M. Stanley et al., A naturalistic open source movie for optical flow evaluation, European Conference on Computer Vision, 2012.

X. Bai, J. Wang, D. Simons, and G. Sapiro, Video snapcut: robust video object cutout using localized classifiers, Transactions on Graphics, vol.28, p.70, 2009.

J. Michael, Y. Black, and . Yacoob, Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion, International Conference on Computer Vision, pp.374-381, 1995.

B. Babenko, M. Yang, and S. Belongie, Visual tracking with online multiple instance learning, Computer Vision Pattern Recognition, pp.983-990, 2009.

Y. Chuang, A. Agarwala, B. Curless, H. David, R. Salesin et al., Video matting of complex scenes, Transactions on Graphics, 2002.

E. S. Calvert, Visual aids for landing in bad visibility with particular reference to the transition from instrument to visual flight, Lighting Research and Technology, vol.15, issue.6, pp.183-219, 1950.

D. Case, Film technology in post production, 2013.

G. Csurka and P. Bouthemy, Direct identification of moving objects and background from 2d motion models, International Conference on Computer Vision, 1999.

A. Crétual and F. Chaumette, Visual servoing based on image motion, The International Journal of Robotics Research, vol.20, issue.11, pp.857-877, 2001.

A. Crétual, F. Chaumette, and P. Bouthemy, Multi-step flow fusion: Towards accurate and dense correspondences in long video shots, British Machine Vision Conference. British Machine Vision Association, vol.2, pp.1251-1254, 1998.

T. Crivelli, P. Conze, P. Robert, and P. Pérez, From optical flow to dense long term correspondences, International Conference on Image Processing, pp.61-64, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00787772

Y. Chuang, B. Curless, H. David, R. Salesin, and . Szeliski, A bayesian approach to digital matting, Computer Vision Pattern Recognition, 2001.

T. Crivelli, M. Fradet, P. Conze, P. Robert, and P. Pérez, Robust optical flow integration, Transactions on Image Processing, vol.24, issue.1, pp.484-498, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01076391

M. Anil and R. Cheriyadat, Non-negative matrix factorization of partial track data for motion segmentation, Computer Vision Pattern Recognition, 2009.

D. Corrigan, A. Robinson, and . Kokaram, Video matting using motion extended grabcut, 2008.

D. Cremers and S. Soatto, Motion competition: A variational approach to piecewise parametric motion segmentation, International Journal of Computer Vision, vol.62, issue.3, pp.249-265, 2005.

F. Dekeyser, P. Bouthemy, P. Pérez, and É. Payot, Superresolution from noisy image sequences exploiting a 2d parametric motion model, International Conference on Pattern Recognition, vol.3, pp.350-353, 2000.

A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas et al., Flownet: Learning optical flow with convolutional networks, International Conference on Computer Vision, pp.2758-2766, 2015.

D. Decarlo and D. Metaxas, Optical flow constraints on deformable models with applications to face tracking, International Journal of Computer Vision, vol.38, issue.2, pp.99-127, 2000.

A. Delong, A. Osokin, N. Hossam, Y. Isack, and . Boykov, Fast approximate energy minimization with label costs, International Journal of Computer Vision, vol.96, issue.1, pp.1-27, 2012.

P. Dollar and L. Zitnick, Sketch tokens: A learned mid-level representation for contour and object detection, Computer Vision Pattern Recognition, 2013.

M. Erofeev, Y. Gitman, D. Vatolin, A. Fedorov, and J. Wang, Perceptually motivated benchmark for video matting, British Machine Vision Conference, 2015.

W. Enkelmann, Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences, Computer Vision, Graphics, and Image Processing, vol.43, pp.150-177, 1988.

V. Estellers and . Soatto, Detecting occlusions as an inverse problem, Journal of Mathematical Imaging and Vision, pp.1-18, 2015.

E. Elhamifar and R. Vidal, Sparse subspace clustering, Computer Vision Pattern Recognition, vol.183, 2009.

K. Fragkiadaki, P. Arbelaez, P. Felsen, and J. Malik, Learning to segment moving objects in videos, Computer Vision Pattern Recognition, 2015.

G. Farnebäck, Fast and accurate motion estimation using orientation tensors and parametric motion models, International Conference on Pattern Recognition, vol.1, pp.135-139, 2000.

G. Farnebäck, Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field, International Conference on Computer Vision, vol.1, pp.171-177, 2001.

G. Farnebäck, Two-frame motion estimation based on polynomial expansion, Image Analysis, pp.363-370, 2003.

D. Fortun, P. Bouthemy, and C. Kervrann, Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow, Computer Vision and Image Understanding, vol.145, pp.1-182, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01001758

D. Fortun, P. Bouthemy, and C. Kervrann, Optical flow modeling and computation: a survey, Computer Vision and Image Understanding, vol.134, pp.1-21, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01104081

D. Fortun, P. Bouthemy, and C. Kervrann, Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow, Computer Vision and Image Understanding, vol.145, pp.81-94, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01001758

D. Fortun, P. Bouthemy, and C. Kervrann, A variational aggregation framework for patch-based optical flow estimation, Journal of Mathematical Imaging and Vision, vol.56, issue.2, pp.280-299, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01408771

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, Learning hierarchical features for scene labeling, Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1915-1929, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00742077

D. Fourure, R. Emonet, E. Fromont, D. Muselet, N. Neverova et al., Multi-task, multi-domain learning: application to semantic segmentation and pose regression, Neurocomputing, vol.251, pp.68-80, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01507132

R. B. Pedro-f-felzenszwalb, D. Girshick, D. Mcallester, and . Ramanan, Object detection with discriminatively trained part-based models, Transactions on Pattern Analysis and Machine Intelligence, 2010.

F. Pedro, . Felzenszwalb, . Daniel, and . Huttenlocher, Efficient belief propagation for early vision, International Journal of Computer Vision, vol.70, issue.1, pp.41-54, 2006.

A. Faktor and M. Irani, Video segmentation by non-local consensus voting, British Machine Vision Conference, 2014.

M. Fleischer, Method of producing moving-picture cartoons, US Patent, vol.1, p.674, 91917-10.

M. Fradet, P. Robert, and P. Pérez, Clustering point trajectories with various life-spans, CVMP, 2009.

J. Fan, X. Shen, and Y. Wu, Scribble tracker: a matting-based approach for robust tracking, Transactions on Pattern Analysis and Machine Intelligence, 2012.

A. Fakih and J. Zelek, Structure from motion: Combining features correspondences and optical flow, International Conference on Pattern Recognition, pp.1-4, 2008.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, Computer Vision Pattern Recognition, 2014.

A. Gilbert, K. Michael, G. M. Giles, . Flachs, Y. Robert-b-rogers et al., A real-time video tracking system, Transactions on Pattern Analysis and Machine Intelligence, pp.47-56, 1980.

. James-j-gibson, The perception of the visual world, 1950.

. James-j-gibson, Visually controlled locomotion and visual orientation in animals, British journal of psychology, vol.49, issue.3, pp.182-194, 1958.

. James-j-gibson, On theories for visual space perception, Scandinavian Journal of Psychology, vol.11, issue.1, pp.75-79, 1970.

. James-j-gibson, On the analysis of change in the optic array, Scandinavian Journal of Psychology, vol.18, issue.1, pp.161-163, 1977.

M. Grundmann, V. Kwatra, M. Han, and I. Essa, Efficient hierarchical graph-based video segmentation, Computer Vision Pattern Recognition, 2010.

G. A. James-j-gibson, H. N. Kaplan, K. Reynolds, and . Wheeler, The change from visible to invisible, Perception, & Psychophysics, vol.5, issue.2, pp.113-116, 1969.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The kitti dataset, The International Journal of Robotics Research, vol.32, issue.11, pp.1231-1237, 2013.

F. García, B. Mirbach, B. Ottersten, F. Grandidier, and A. Cuesta, Pixel weighted average strategy for depth sensor data fusion, International Conference on Image Processing, pp.2805-2808, 2010.

S. L. Eduardo, M. Gastal, and . Oliveira, Shared sampling for real-time alpha matting, Computer Graphics Forum, 2010.

L. Grady and T. Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting, VIIP, 2005.

J. B. Samuel-j-gershman, F. Tenenbaum, and . Jäkel, Discovering hierarchical motion structure, Vision Research, 2015.

S. Hsu, S. Anandan, and . Peleg, Accurate computation of optical flow by using layered motion representations, International Conference on Pattern Recognition, vol.1, pp.743-746, 1994.

F. Heitz and P. Bouthemy, Multimodal estimation of discontinuous optical flow using markov random fields, Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.12, pp.1217-1232, 1993.
URL : https://hal.archives-ouvertes.fr/inria-00075193

R. João-f-henriques, P. Caseiro, J. Martins, and . Batista, Kernelized correlation filters. Transactions on Pattern Analysis and Machine Intelligence, 2015.

G. Huang, Z. Liu, Q. Kilian, L. Weinberger, and . Van-der-maaten, Densely connected convolutional networks, 2016.

A. Humayun, O. Mac-aodha, and G. Brostow, Learning to find occlusion regions, Computer Vision Pattern Recognition, 2011.

M. Haag and H. Nagel, Combination of edge element and optical flow estimates for 3d-model-based vehicle tracking in traffic image sequences, International Journal of Computer Vision, vol.35, issue.3, pp.295-319, 1999.

F. Heitz, P. Pérez, and P. Bouthemy, Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun. A global sampling method for alpha matting, Computer Vision Pattern Recognition, vol.59, pp.125-134, 1994.

K. P. Berthold, B. G. Horn, and . Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.

S. Hare, A. Saffari, and P. Torr, Struck: Structured output tracking with kernels, International Conference on Computer Vision, pp.263-270, 2011.

J. Peter and . Huber, Robust statistics, International Encyclopedia of Statistical Science, pp.1248-1251, 2011.

W. Paul, R. E. Holland, and . Welsch, Robust regression using iteratively reweighted least-squares, Communications in Statistics-theory and Methods, vol.6, issue.9, pp.813-827, 1977.

R. Hartley and A. Zisserman, Multiple view geometry in computer vision, 2003.

J. Huang, T. Zhang, and D. Metaxas, Learning with structured sparsity, The Journal of Machine Learning Research, vol.12, pp.3371-3412, 2011.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Computer Vision Pattern Recognition, pp.770-778, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Identity mappings in deep residual networks, European Conference on Computer Vision, pp.630-645, 2016.

S. Ince and J. Konrad, Occlusion-aware optical flow estimation, Transactions on Image Processing, vol.17, issue.8, pp.1443-1451, 2008.
DOI : 10.1109/tip.2008.925381

E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy et al., Flownet 2.0: Evolution of optical flow estimation with deep networks, Computer Vision Pattern Recognition, 2017.

K. Jarrett, Beyond broadcast yourself: the future of youtube, Media International Australia, vol.126, issue.1, pp.132-144, 2008.

X. Shanon, . Ju, J. Michael, A. Black, and . Jepson, Skin and bones: Multi-layer, locally affine, optical flow and regularization with transparency, Computer Vision Pattern Recognition, 1996.

N. Jacobson, Y. Freund, and . Truong-q-nguyen, An online learning approach to occlusion boundary detection, Transactions on Image Processing, vol.21, issue.1, pp.252-261, 2012.
DOI : 10.1109/tip.2011.2162420

J. Yu-jason, A. W. Harley, and . Derpanis, Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness, ECCV Workshop, pp.3-10, 2016.

Z. Jiang, Z. Lin, and L. Davis, Learning a discriminative dictionary for sparse coding via label consistent k-svd, CVPR, 2011.
DOI : 10.1109/cvpr.2011.5995354

URL : http://www.umiacs.umd.edu/%7Elsd/papers/CVPR2011_LCKSVD_final.pdf

R. Jenatton, J. Mairal, G. Obozinki, and F. Bach, Proximal methods for sparse hierarchical dictionary learning, International Conference on Machine Learning, 2010.

G. Johansson, Configurations in the perception of velocity, Acta Psychologica, vol.7, pp.25-79, 1950.

G. Johansson, On theories for visual space perception, Scandinavian Journal of Psychology, vol.11, issue.1, pp.67-74, 1970.
DOI : 10.1111/j.1467-9450.1970.tb00719.x

G. Johansson, Visual perception of biological motion and a model for its analysis, Perception, & Psychophysics, vol.14, issue.2, pp.201-211, 1973.

V. Jain and S. Seung, Natural image denoising with convolutional networks, Conference on Neural Information Processing Systems, pp.769-776, 2009.

M. Jain, J. Van-gemert, H. Jégou, P. Bouthemy, G. M. Cees et al., Action localization with tubelets from motion, Computer Vision Pattern Recognition, 2014.
DOI : 10.1109/cvpr.2014.100

URL : https://hal.archives-ouvertes.fr/hal-00996844

M. Jain, J. Van-gemert, H. Jégou, P. Bouthemy, G. M. Cees et al., Tubelets: Unsupervised action proposals from spatiotemporal super-voxels, International Journal of Computer Vision, pp.1-25, 2017.
DOI : 10.1007/s11263-017-1023-9

URL : https://hal.archives-ouvertes.fr/hal-01561056

M. Keuper, B. Andres, and T. Brox, Motion trajectory segmentation via minimum cost multicuts, International Conference on Computer Vision, 2015.
DOI : 10.1109/iccv.2015.374

D. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

J. Kopf, D. Michael-f-cohen, M. Lischinski, and . Uyttendaele, Joint bilateral upsampling, vol.26, issue.3, p.96, 2007.

P. Krähenbühl and V. Koltun, Efficient inference in fully connected CRFs with Gaussian edge potentials, Conference on Neural Information Processing Systems, 2011.

P. Krähenbühl and V. Koltun, Parameter learning and convergent inference for dense random fields, International Conference on Machine Learning, 2013.

M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, G. Luka?ehovinluka?luka?ehovin et al., Aleksandar Dimitriev, et al. The visual object tracking VOT2014 challenge results, ECCV Workshop, pp.191-217, 2014.

E. Erum-arif-khan, . Reinhard, W. Roland, H. Fleming, and . Bülthoff, Image-based material editing, Transactions on Graphics, 2006.

J. Philip, E. S. Kellman, and . Spelke, Perception of partly occluded objects in infancy, Cognitive Psychology, vol.15, issue.4, pp.483-524, 1983.

I. Kitahara, H. Saito, S. Akimichi, T. Ono, Y. Ohta et al., Large-scale virtualized reality. CVPR Workshop, 2001.

B. Sing, R. Kang, J. Szeliski, and . Chai, Handling occlusions in dense multi-view stereo, Computer Vision Pattern Recognition, 2001.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Conference on Neural Information Processing Systems, pp.1097-1105, 2012.

P. Kohli, . Philip, and . Torr, Dynamic graph cuts for efficient inference in Markov random fields. Transactions on Pattern Analysis and Machine Intelligence, 2007.

A. Kundu, V. Vineet, and V. Koltun, Feature space optimization for semantic video segmentation, Computer Vision Pattern Recognition, pp.3168-3175, 2016.

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, 1988.

V. Kolmogorov and R. Zabih, Computing visual correspondence with occlusions using graph cuts, International Conference on Computer Vision, 2001.

Y. Lu, X. Bai, L. Shapiro, and J. Wang, Coherent parametric contours for interactive video object segmentation, Computer Vision Pattern Recognition, 2016.

D. Li, Q. Chen, and C. Tang, Motion-aware knn laplacian for video matting, International Conference on Computer Vision, 2013.

M. Levoy, Light fields and computational imaging, Computer, vol.39, issue.8, pp.46-55, 2006.

H. and K. Prazdny, The interpretation of a moving retinal image, Proceedings of the Royal Society of London B: Biological Sciences, vol.208, pp.385-397, 1173.

X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick et al., A survey of appearance models in visual object tracking, Transactions on Intelligent Systems and Technology, vol.4, issue.4, p.58, 2013.

D. Bruce, T. Lucas, and . Kanade, An iterative image registration technique with an application to stereo vision, International Joint Conference on Artifical Intelligence, pp.674-679, 1981.

Y. Lee, J. Kim, and K. Grauman, Key-segments for video object segmentation, International Conference on Computer Vision, 2011.

A. Levin, D. Lischinski, and Y. Weiss, A closed-form solution to natural image matting. Transactions on Pattern Analysis and Machine Intelligence, 2008.

V. Lempitsky, C. Rother, S. Roth, and A. Blake, Fusion moves for markov random field optimization. Transactions on Pattern Analysis and Machine Intelligence, vol.32, pp.1392-1405, 2010.

W. Li, F. Viola, J. Starck, J. Gabriel, . Brostow et al., Roto++: Accelerating professional rotoscoping using shape manifolds, Transactions on Graphics, vol.35, issue.4, p.62, 2016.

J. Lim and M. Yang, A direct method for modeling non-rigid motion with thin plate spline, Computer Vision Pattern Recognition, vol.1, pp.1196-1202, 2005.

C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. Freeman, Sift flow: Dense correspondence across different scenes, European Conference on Computer Vision, pp.28-42, 2008.

G. François, P. Meyer, and . Bouthemy, Region-based tracking using affine motion models in long image sequences, CVGIP: Image understanding, vol.60, issue.2, pp.119-140, 1994.

J. Matas, O. Chum, M. Urban, and T. Pajdla, Robust widebaseline stereo from maximally stable extremal regions. Image and vision computing, 2004.

Q. Mo, . Bruce, and . Draper, Semi-nonnegative matrix factorization for motion segmentation with missing data, European Conference on Computer Vision, 2012.

M. Menze and A. Geiger, Object scene flow for autonomous vehicles, Computer Vision Pattern Recognition, 2015.

C. Ma, J. Huang, X. Yang, and M. Yang, Hierarchical convolutional features for visual tracking, International Conference on Computer Vision, pp.3074-3082, 2015.

I. Mikic, S. Krucinski, and J. Thomas, Segmentation and tracking in echocardiographic sequences: Active contours guided by optical flow estimates, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.274-284, 1998.

D. Marr and T. Poggio, Cooperative computation of stereo disparity, 1976.

E. Mémin and P. Pérez, Dense estimation and object-based segmentation of the optical flow with robust techniques, Transactions on Image Processing, vol.7, issue.5, pp.703-719, 1998.

N. Märki, F. Perazzi, O. Wang, and A. Sorkine-hornung, Bilateral space video segmentation, Computer Vision Pattern Recognition, pp.743-751, 2016.

N. Märki, F. Perazzi, O. Wang, and A. Sorkine-hornung, Bilateral space video segmentation, Computer Vision Pattern Recognition, 2016.

F. Mériaudeau, R. Rantoson, D. Fofi, and C. Stolz, Review and comparison of non-conventional imaging systems for threedimensional digitization of transparent objects, Journal of Electronic Imaging, vol.21, issue.2, pp.21105-21106, 2012.

B. Morris and M. Trivedi, Learning trajectory patterns by clustering: Experimental studies and comparative evaluation, Computer Vision Pattern Recognition, pp.312-319, 2009.

K. Nakayama and J. M. Loomis, Optical velocity patterns, velocity-sensitive neurons, and space perception: a hypothesis, Perception, vol.3, issue.1, pp.63-80, 1974.

Y. Niu, Z. Xu, and X. Che, Dynamically removing false features in pyramidal lucas-kanade registration, Transactions on Image Processing, vol.23, issue.8, pp.3535-3544, 2014.

A. Newell, K. Yang, and J. Deng, Stacked hourglass networks for human pose estimation, European Conference on Computer Vision, pp.483-499, 2016.

J. , M. Odobez, and P. Bouthemy, Robust multiresolution estimation of parametric motion models, Journal of Visual Communication and Image Representation, vol.6, issue.4, pp.348-365, 1995.

J. M. Odobez and . Bouthemy, Separation of moving regions from background in an image sequence acquired with a mobile camera, Video Data Compression for Multimedia Computing, pp.283-311, 1997.

J. , M. Odobez, and P. Bouthemy, Direct incremental model-based image motion segmentation for video analysis, Signal Processing, vol.66, issue.2, pp.143-155, 1998.

P. Ochs and T. Brox, Higher order motion models and spectral clustering, Computer Vision Pattern Recognition, 2012.

A. Odena, V. Dumoulin, and C. Olah, Deconvolution and checkerboard artifacts, 2016.

J. , M. Odobez, and D. Gatica-perez, Motion likelihood and proposal modeling in model-based stochastic tracking, Transactions on Image Processing, 2006.

P. Ochs, J. Malik, and T. Brox, Segmentation of moving objects by long term video analysis. Transactions on Pattern Analysis and Machine Intelligence, vol.36, pp.1187-1200, 2014.

P. Pérez, A. Blake, and M. Gangnet, Jetstream: Probabilistic contour extraction with particles, International Conference on Computer Vision, 2001.

A. Papazoglou and V. Ferrari, Fast object segmentation in unconstrained video, International Conference on Computer Vision, pp.1777-1784, 2013.

J. Pan and B. Hu, Robust occlusion handling in object tracking, Computer Vision Pattern Recognition, 2007.

Y. Park, V. Lepetit, and W. Woo, Extended keyframe detection with stable tracking for multiple 3d object tracking, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.11, pp.1728-1735, 2011.

F. Perazzi, J. Pont-tuset, B. Mcwilliams, L. Van-gool, M. Gross et al., A benchmark dataset and evaluation methodology for video object segmentation, Computer Vision Pattern Recognition, 2016.

J. Pérez-rúa, A. Basset, and P. Bouthemy, Detection and localization of anomalous motion in video sequences from local histograms of labeled affine flows, Frontiers in ICT, vol.4, issue.10, 2017.

J. Pérez-rúa, T. Crivelli, P. Bouthemy, and P. Pérez, Determining occlusions from space and time image reconstructions, Computer Vision Pattern Recognition, 2016.

J. Pérez-rúa, T. Crivelli, P. Bouthemy, and P. Pérez, Trimap tracking: closing the gap between rotoscoping and alpha matting, 2017.

J. Perez-rua, T. Crivelli, P. Bouthemy, and P. Perez, Learning how to be robust: Deep polynomial regression, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01923068

J. Pérez-rúa, T. Crivelli, and P. Pérez, Backgroundforeground tracking for video object segmentation, International Conference on Image Processing, pp.1613-1617, 2015.

J. Pérez-rúa, T. Crivelli, and P. Pérez, Object-guided motion estimation, Computer Vision and Image Understanding, vol.153, pp.88-99, 2016.

J. Pérez-rúa, T. Crivelli, P. Pérez, and P. Bouthemy, Discovering motion hierarchies via tree-structured coding of trajectories, British Machine Vision Conference, 2016.

J. Perez-rua, T. Crivelli, P. Perez, and P. Bouthemy, Hierarchical motion decomposition for dynamic scene parsing, International Conference on Image Processing, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01314095

J. Pérez-rúa, O. Miksik, H. S. Philip, P. Torr, and . Pérez, Roam: a rich object appearance model with application to rotoscoping, Computer Vision Pattern Recognition, 2017.

H. Park, T. Schoepflin, and Y. Kim, Active contour model with gradient directional information: Directional snake. Transactions on Circuits and Systems for Video Technology, vol.11, pp.252-256, 2001.

T. Poggio, V. Torre, and C. Koch, Computational vision and regularization theory, Nature, vol.317, issue.6035, pp.314-319, 1985.

M. Proesmans, L. Van-gool, E. Pauwels, and A. Oosterlinck, Determination of optical flow and its discontinuities using non-linear diffusion, European Conference on Computer Vision, 1994.
DOI : 10.1007/bfb0028362

A. Rav-acha, P. Kohli, C. Rother, and A. Fitzgibbon, Unwrap mosaics: A new representation for video editing, Transactions on Graphics, 2008.

I. Rocco, R. Arandjelovi´carandjelovi´c, J. Sivic, ;. Raza, A. Humayun et al., Finding temporally consistent occlusion boundaries in videos using geometric context, Winter Conference on Applications of Computer Vision, 2015.

C. Rother, V. Kolmogorov, and A. Blake, Grabcut: Interactive foreground extraction using iterated graph cuts, Transactions on Graphics, vol.23, pp.309-314, 2004.

M. Rubinstein, C. Liu, and W. Freeman, Towards longer long-range motion trajectories, British Machine Vision Conference, 2012.
DOI : 10.5244/c.26.53

C. Rhemann, C. Rother, and M. Gelautz, Improving color modeling for alpha matting, British Machine Vision Conference, 2008.
DOI : 10.5244/c.22.115

R. Shankar-r-rao, R. Tron, Y. Vidal, and . Ma, Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories, Computer Vision Pattern Recognition, 2008.

J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, Deepmatching: Hierarchical deformable dense matching. International Journal of Computer Vision, 2015.
DOI : 10.1007/s11263-016-0908-3

URL : https://hal.archives-ouvertes.fr/hal-01148432

J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow, Computer Vision Pattern Recognition, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01097477

J. Shi, Good features to track, Computer Vision Pattern Recognition, pp.593-600, 1994.

N. Sundaram, T. Brox, and K. Keutzer, Dense point trajectories by gpu-accelerated large displacement optical flow, European Conference on Computer Vision, pp.438-451, 2010.
DOI : 10.1007/978-3-642-15549-9_32

URL : http://nma.berkeley.edu/ark:/28722/bk00071397c

N. Sundaram, T. Brox, K. Sundberg, T. Brox, M. Maire et al., Dense point trajectories by gpu-accelerated large displacement optical flow, Computer Vision Pattern Recognition, 2010.
DOI : 10.1007/978-3-642-15549-9_32

URL : http://nma.berkeley.edu/ark:/28722/bk00071397c

L. Mehmet-emre-sargin, . Bertelli, S. Bangalore, K. Manjunath, and . Rose, Probabilistic occlusion boundary detection on spatio-temporal lattices, International Conference on Computer Vision, 2009.

W. M. Arnold, D. M. Smeulders, R. Chu, S. Cucchiara, A. Calderara et al., Visual tracking: An experimental survey, Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.7, pp.1442-1468, 2014.

M. Steven, B. Seitz, J. Curless, D. Diebel, R. Scharstein et al., A comparison and evaluation of multi-view stereo reconstruction algorithms, Computer Vision Pattern Recognition, vol.1, pp.519-528, 2006.

P. Smith, T. Drummond, and R. Cipolla, Layered motion segmentation and depth ordering by tracking edges, Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.4, pp.479-494, 2004.
DOI : 10.1109/tpami.2004.1265863

URL : http://mi.eng.cam.ac.uk/%7Ecipolla/publications/article/2004-PAMI-motionsegmentation.pdf

T. Senst, T. Volker-eiselein, and . Sikora, Robust local optical flow for feature tracking. Transactions on Circuits and Systems for Video Technology, vol.22, pp.1377-1387, 2012.
DOI : 10.1109/tcsvt.2012.2202070

C. Strecha, R. Fransens, and L. Van-gool, A probabilistic approach to large displacement optical flow and occlusion detection, Statistical methods in video processing, pp.71-82, 2004.
DOI : 10.1007/978-3-540-30212-4_7

C. Stauffer and E. Grimson, Adaptive background mixture models for real-time tracking, Computer Vision Pattern Recognition, 1999.
DOI : 10.1109/cvpr.1999.784637

N. Andrew, M. Stein, and . Hebert, Occlusion boundaries from motion: Lowlevel detection and mid-level reasoning, International Journal of Computer Vision, vol.82, issue.3, pp.325-357, 2009.

J. Sun, Y. Li, B. Sing, H. Kang, and . Shum, Symmetric stereo matching for occlusion handling, Computer Vision Pattern Recognition, 2005.

D. Sun, C. Liu, and H. Pfister, Local layering for joint motion estimation and occlusion detection, Computer Vision Pattern Recognition, pp.1098-1105, 2014.

J. Robert, E. S. Schalkoff, and . Mcvey, A model and tracking algorithm for a class of video targets, Transactions on Pattern Analysis and Machine Intelligence, issue.1, pp.2-10, 1982.

N. Shankar-nagaraja, R. Frank, T. Schmidt, and . Brox, Video segmentation with just a few strokes, International Conference on Computer Vision, 2015.

D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, Computer Vision Pattern Recognition, pp.2432-2439, 2010.

M. Shah, P. Krishnan-rangarajan, and . Tsai, Motion trajectories, IEEE Transactions on Systems, Man, and Cybernetics, vol.23, issue.4, pp.1138-1150, 1993.

D. Scharstein and R. Szeliski, High-accuracy stereo depth maps using structured light, Computer Vision Pattern Recognition, vol.1, 2003.

D. Sun, E. B. Sudderth, and M. Black, Layered image motion with explicit occlusions, temporal consistency, and depth ordering, Conference on Neural Information Processing Systems, 2010.

D. Sun, E. B. Sudderth, and M. Black, Layered segmentation and optical flow estimation over time, Computer Vision Pattern Recognition, 2012.

D. Sun, E. B. Sudderth, and H. Pfister, Layered rgbd scene flow estimation, Computer Vision Pattern Recognition, pp.548-556, 2015.

D. Sun, X. Yang, M. Liu, and J. Kautz, Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume, 2017.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

Y. Gregory and . Tang, A discrete version of Green's theorem. Transactions on Pattern Analysis and Machine Intelligence, 1982.

M. Tao, J. Bai, P. Kohli, and S. Paris, Simpleflow: A non-iterative, sublinear optical flow algorithm, Computer Graphics Forum, vol.31, pp.345-353, 2012.

D. Tsai, M. Flagg, and J. M. Rehg, Motion coherent tracking with multi-label mrf optimization, British Machine Vision Conference, 2010.

D. Tsai, M. Flagg, A. Nakazawa, and J. Rehg, Motion coherent tracking using multi-label mrf optimization, International Journal of Computer Vision, vol.100, issue.2, pp.190-202, 2012.

T. Tommasini, A. Fusiello, E. Trucco, and V. Roberto, Making good features track better, Computer Vision Pattern Recognition, pp.178-183, 1998.

A. Joel, A. Tropp, and . Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, Transactions on Information Theory, vol.53, issue.12, pp.4655-4666, 2007.

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

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, International Conference on Computer Vision, 1998.

J. Tighe, M. Niethammer, and S. Lazebnik, Scene parsing with object instances and occlusion ordering, Computer Vision Pattern Recognition, 2014.

R. Tron and R. Vidal, A benchmark for the comparison of 3-d motion segmentation algorithms, Computer Vision Pattern Recognition, 2007.

Y. Tsai, M. Yang, and M. J. Black, Video segmentation via object flow, Computer Vision Pattern Recognition, 2016.

J. Thewlis, S. Zheng, H. S. Philip, A. Torr, and . Vedaldi, Fully-trainable deep matching, British Machine Vision Conference, 2016.

H. Uemura, S. Ishikawa, and K. Mikolajczyk, Feature tracking and motion compensation for action recognition, British Machine Vision Conference, pp.1-10, 2008.

R. Vidal and R. Hartley, Motion segmentation with missing data using powerfactorization and GPCA, Computer Vision Pattern Recognition, 2004.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, The Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

R. Vidal and Y. Ma, A unified algebraic approach to 2-D and 3-D motion segmentation, European Conference on Computer Vision, 2004.

A. Verri and T. Poggio, Motion field and optical flow: Qualitative properties, Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.5, pp.490-498, 1989.

Y. A. John, . Wang, and . Edward-h-adelson, Layered representation for motion analysis, Computer Vision Pattern Recognition, pp.361-366, 1993.

J. Wang and . Michael-f-cohen, Image and video matting: a survey. Foundations and Trends® in Computer Graphics and Vision, vol.3, pp.97-175, 2008.

H. Wang, A. Kläser, C. Schmid, and C. Liu, Action recognition by dense trajectories, Computer Vision Pattern Recognition, pp.3169-3176, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00583818

Y. Wu, J. Lim, and M. Yang, Online object tracking: A benchmark, Computer Vision Pattern Recognition, pp.2411-2418, 2013.

J. Weber and J. Malik, Robust computation of optical flow in a multi-scale differential framework, International Journal of Computer Vision, vol.14, issue.1, pp.67-81, 1995.

Y. Wang and G. Mori, Multiple tree models for occlusion and spatial constraints in human pose estimation, European Conference on Computer Vision, 2008.

S. Wu, B. E. Moore, and M. Shah, Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes, Computer Vision Pattern Recognition, pp.2054-2060, 2010.

B. Wu, R. Nevatia, and Y. Li, Segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses, Computer Vision Pattern Recognition, 2008.

D. Weinland, M. Özuysal, and P. Fua, Making action recognition robust to occlusions and viewpoint changes, European Conference on Computer Vision, 2010.

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, Deepflow: Large displacement optical flow with deep matching, International Conference on Computer Vision, pp.1385-1392, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00873592

S. Wright, Digital compositing for film and video, 2006.

J. Donna, M. Williams, and . Shah, A fast algorithm for active contours and curvature estimation. Computer Vision and Image Understanding, 1992.

J. Wang, B. Thiesson, Y. Xu, and M. Cohen, Image and video segmentation by anisotropic kernel mean shift, European Conference on Computer Vision, 2004.

C. Xu and J. J. Corso, Evaluation of super-voxel methods for early video processing, Computer Vision Pattern Recognition, 2012.

L. Xu, J. Chen, and J. Jia, A segmentation based variational model for accurate optical flow estimation, European Conference on Computer Vision, 2008.

J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi, Bilateral filtering-based optical flow estimation with occlusion detection, European Conference on Computer Vision, 2006.

L. Xu, J. Jia, and Y. Matsushita, Motion detail preserving optical flow estimation. Transactions on Pattern Analysis and Machine Intelligence, vol.34, pp.1744-1757, 2012.

N. Xu, B. Price, S. Cohen, and T. Huang, Deep image matting, Computer Vision Pattern Recognition, 2017.

C. Xu, C. Xiong, and J. J. Corso, Streaming hierarchical video segmentation, European Conference on Computer Vision, 2012.

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

J. Yang and H. Li, Dense, accurate optical flow estimation with piecewise parametric model, Computer Vision Pattern Recognition, 2015.

A. Yilmaz, X. Li, and M. Shah, Contour-based object tracking with occlusion handling in video acquired using mobile cameras, Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1531-1536, 2004.

S. Yamamoto, Y. Mae, Y. Shirai, and J. Miura, Realtime multiple object tracking based on optical flows, International Conference on Robotics and Automation, vol.3, pp.2328-2333, 1995.

T. Yu, C. Zhang, M. Cohen, Y. Rui, and Y. Wu, Monocular video foreground/background segmentation by tracking spatial-color gaussian mixture models. In Motion and Video Computing, IEEE Workshop on, 2007.

Z. Zivkovic, Improved adaptive gaussian mixture model for background subtraction, International Conference on Pattern Recognition, vol.2, pp.28-31, 2004.

H. Zhang, B. Li, and D. Yang, Keyframe detection for appearance-based visual slam, International Conference on Intelligent Robots and Systems, pp.2071-2076, 2010.

Y. Zheng, S. Neo, T. Chua, and Q. Tian, The use of temporal, semantic and visual partitioning model for efficient near-duplicate keyframe detection in large scale news corpus, International Conference on Image and Video Retrieval, pp.409-416, 2007.

C. Zach, T. Pock, and H. Bischof, A duality based approach for realtime TV-L1 optical flow, Pattern Recognition, pp.214-223, 2007.

P. Zhao, G. Rocha, and B. Yu, The composite absolute penalties family for grouped and hierarchical variable selection. The Annals of Statistics, pp.3468-3497, 2009.