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Publié par | Thesee |
Nombre de lectures | 17 |
Langue | English |
Poids de l'ouvrage | 9 Mo |
Extrait
ECOLE CENTRALE DE PARIS
P H D T H E S I S
to obtain the title of
PhD of Science
of Ecole Centrale de Paris
Specialty : Applied Mathematics
Defended by
Martinde La Gorce
Model-based 3D Hand Pose
Estimation from Monocular
Video
Thesis Advisor: Nikos Paragios
prepared at Ecole Centrale de Paris, MAS laboratory
defended on December 14, 2009
Jury :
Reviewers : Dimitri Metaxas - Rutgers University
Pascal Fua - EPFL
Advisor : Nikos Paragios - Ecole Centrale de Paris
Examinators : Radu Patrice Horaud - INRIA
Renaud Keriven - Ecole de Ponts Paritech
Adrien Bartoli - University d’Auvergne
Bjorn Stenger - Toshiba Research
Invited : David Fleet - University of Toronto.
tel-00619637, version 1 - 6 Sep 2011tel-00619637, version 1 - 6 Sep 2011Acknowledgments
I would like to thank the people that helped me during preparing my PhD these
last four years.
Thank to my PhD advisor, Nikos Paragios, who has been supportive and had
confidenceinmywork. HegavemethefreedomIneeded,proposedmeveryrelevant
directions and has been precious in is help to communicate and yield visibility to
my research results. I also greatly appreciated his encouragements to establish
international collaborations by visiting other prestigious research centers.
Thank to David Fleet for the extremely productive collaboration we started
during a two month visit in the university of Toronto. While discussing the results
I obtained with the method presented in the third chapter of this manuscript,
he made a simple but sound remark that motivated the direction taken in the
fourth chapter: “if you needed to add shading onto your hand model to get a
good visualization of your results, that means that you should add shading in the
generative model you use for the tracking”. I deeply appreciated his strive for
perfectionintheexplanationofscientificideasandhisenthusiasmtodiscussabout
in-depthtechnicalaspectsaswellasthegreatchallengesofthefieldingeneral. His
help as also be precious in the writing of this manuscript.
I am grateful to my thesis rapporteurs Dimitri Metaxas and Pascal Fua, for
havingkindlyacceptedtoreviewthiswork. Iappreciatedtheirvaluablecomments.
ThanktoRaduPatriceHoraud,RenaudKeriven,AdrienBartolyandBjornStenger
for examining it and for the constructive discussion during the thesis defense.
Thanks to all the current and former members of the Medical Imaging and
ComputerVisionGroupattheAppliedMathematicsDepartmentinEcoleCentrale
for the friendly international environment and the great working atmosphere. In
particularIwouldliketothankChaohuiWangandMicka¨elSavinaudforourfruitful
collaboration. Thank to Noura and Regis for their moral support. Thanks to my
friendsGeoffrayandRomainforhavingconfirmingmebytheirexampleintheidea
that having hobbies is a necessary condition to do a good PhD thesis. And finally,
thank to all my other friend and my family members who have been supporting
during these four years.
tel-00619637, version 1 - 6 Sep 2011tel-00619637, version 1 - 6 Sep 2011Index
1 Introduction 5
1.1 General introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Applications of Hand tracking . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Animation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Quantitative Motions analysis . . . . . . . . . . . . . . . . . . 8
1.2.3 Sign Language Recognition . . . . . . . . . . . . . . . . . . . 8
1.2.4 2D human-Computer interaction . . . . . . . . . . . . . . . . 10
1.2.5 3D human-Computer interaction . . . . . . . . . . . . . . . . 10
1.2.6 The hand as a high DOF control device . . . . . . . . . . . . 11
1.3 Hand Pose Estimation Scientific Challenges . . . . . . . . . . . . . . 11
1.4 Contributions & outline . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.4.1 Part-based Hand Representation and Statistical Inference . . 15
1.4.2 Triangular Mesh with Texture & Shading . . . . . . . . . . . 16
1.4.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 State of the art 19
2.1 Acquisition framework . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.1 Monocular setting . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.2 Small baseline stereo setting. . . . . . . . . . . . . . . . . . . 20
2.1.3 Wide baseline setting . . . . . . . . . . . . . . . . . . . . . . 20
2.1.4 Other settings . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1.5 Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1.6 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Model-based tracking. . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.1 General principle . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Hand models . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.3 Images features . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.4 Fitting procedures . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3 Discriminative methods / Learning-Based Methods . . . . . . . . . . 43
2.3.1 Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.3.2 Database indexing methods . . . . . . . . . . . . . . . . . . . 44
2.3.3 Regression techniques . . . . . . . . . . . . . . . . . . . . . . 46
2.4 Other approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5 Related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.5.1 Non-optical systems . . . . . . . . . . . . . . . . . . . . . . . 48
2.6 Limitations of existing methods . . . . . . . . . . . . . . . . . . . . . 49
tel-00619637, version 1 - 6 Sep 2011iv Index
3 Silhouette Based Method 53
3.1 Method overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2 Articulated model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2.1 Forward kinematic . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2.2 Forward Kinematic Differentiation . . . . . . . . . . . . . . . 61
3.2.3 Hand anatomy terms . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.4 The hand skeleton model . . . . . . . . . . . . . . . . . . . . 65
3.2.5 Linear constraints on joint angles . . . . . . . . . . . . . . . . 67
3.2.6 Model calibration. . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3 Hand surface model and projection . . . . . . . . . . . . . . . . . . . 70
3.3.1 surface model . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.3.2 Camera model . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.3.3 Ellipsoid projection. . . . . . . . . . . . . . . . . . . . . . . . 74
3.3.4 Convex polytope projection . . . . . . . . . . . . . . . . . . . 75
3.3.5 Filled ellipses/polygons union . . . . . . . . . . . . . . . . . . 76
3.3.6 Intersecting two ellipses . . . . . . . . . . . . . . . . . . . . . 80
3.3.7 Intersecting an ellipse with a polyline . . . . . . . . . . . . . 80
3.3.8 Intersecting boundaries of two polygons . . . . . . . . . . . . 81
3.4 Matching cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.4.1 Generative colors models . . . . . . . . . . . . . . . . . . . . 84
3.4.2 The discontinuous likelihood . . . . . . . . . . . . . . . . . . 85
3.4.3 The continuous likelihood . . . . . . . . . . . . . . . . . . . . 88
3.5 Numerical computation of the matching cost . . . . . . . . . . . . . 92
3.5.1 Line segments. . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.5.2 Ellipsoid arcs . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.5.3 Approximating filled-ellipses by polygons . . . . . . . . . . . 98
3.6 The Matching Cost Derivatives . . . . . . . . . . . . . . . . . . . . . 100
3.6.1 Differentiation of the polytope transformation and projection 101
3.6.2 Differentiation of the ellipsoid transformation and projection 101
3.6.3 Differentiation of ellipses to convex polygons conversion . . . 102
3.6.4 Differentiation of segment intersections. . . . . . . . . . . . . 103
3.6.5 Force on silhouette vertices . . . . . . . . . . . . . . . . . . . 103
3.6.6 Second order derivatives . . . . . . . . . . . . . . . . . . . . . 106
3.7 Pose estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.7.1 Sequential Quadratic Programing with BFGS update . . . . 108
3.7.2 Variable metric descent . . . . . . . . . . . . . . . . . . . . . 110
3.7.3 Trust-Region method . . . . . . . . . . . . . . . . . . . . . . 112
3.7.4 Comparing the three Optimization methods . . . . . . . . . . 113
3.7.5 Exact versus Approximate Matching cost and derivatives . . 115
3.7.6 Smart Particle Filtering . . . . . . . . . . . . . . . . . . . . . 118
3.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.8.1 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.8.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
tel-00619637, version 1 - 6 Sep 2011Index v
4 Method with texture & shading 127
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.2 Hand geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
4.2.1 The choice of triangulated surface . . . . . . . . . . . . . . . 129
4.2.2 Linear Blend Skinning . . . . . . . . . . . . . . . . . . . .