A COMPARISON AND EVALUATION OF MOTION INDEXING ...

More documents

Recommendations

Info

ACKNOWLEDGEMENTS The successful completion of this thesis is due to the invaluable guidance, support and inspiration from a number of people. I would like to express my profound gratitude to my advisor Dr.Gutemberg Guerra-Filho for his continuous support, encouragement, supervision and valuable suggestions throughout this research work. His direction and guidance played an important role in the successful completion of this thesis. I would like to thank CMU Graphics Lab Motion Capture database for the motion capture data files which I used for testing my experiments. The database was created with funding from NSF EIA-0196217. I would like to sincerely thank Dr. Manfred Huber and Dr. Jean Gao for taking interest in my work and agreeing to be on my thesis defense committee. I would like to thank Dr. Farhad Kamangar for being an excellent teacher for the Computer Graphics course which was very important in the completion of this thesis. I would like to thank my friend Rohit Rawat for all the help he provided towards successfully completing my thesis work. And most importantly, I would like to thank my family for always giving me all the support and motivation I needed towards pursuing my interests and achieving excellence in all my work including this thesis. iv May 17, 2010
ABSTRACT A COMPARISON AND EVALUATION OF MOTION INDEXING TECHNIQUES HARNISH BHATIA, M.S. The University of Texas at Arlington, 2010 Supervising Professor: Gutemberg Guerra Filho Motion capture (mocap) is a way to digitally represent the spatio-temporal structure of human movement. Human motion is generally captured in long sequences to record the natural and complex aspects of human actions, its sequential transitions, and simultaneous combinations. As the amount of mocap data increases, it becomes important to index the data in order to improve the performance of information access. The motion indexing problem involves several challenging issues due to the data being high dimensional, continuous, and time-variant. Indexing provides the means to search for similar motion in a mocap database, to recognize a query action as one among several motion classes, and fosters the reusability of motion data to generate animation automatically. The topic of my research is the design, implementation, and evaluation of several approaches to the motion indexing problem. We consider three different existing types of whole-body motion indexing algorithms: dimensionality reduction based techniques, feature function based techniques, and dynamic time warping based techniques. The advantages and disadvantages of these techniques are explored. We evaluate the performance of each technique using a subset of the CMU Motion Cap- v
Page 1 and 2: A COMPARISON AND EVALUATION OF MOTI
Page 3: To my mom, dad, brother, friends an
Page 7 and 8: TABLE OF CONTENTS ACKNOWLEDGEMENTS
Page 9 and 10: LIST OF FIGURES Figure Page 1.1 Ske
Page 11 and 12: CHAPTER 1 INTRODUCTION Animation is
Page 13 and 14: • Segmentation: The motion captur
Page 15 and 16: In several applications, like a mod
Page 17 and 18: provide fast interpolation but do n
Page 19 and 20: • Frame: Each slice of time of a
Page 21 and 22: Figure 1.1: Skeleton with bone name
Page 23 and 24: Figure 1.4: Snapshot of a sample AM
Page 25 and 26: CHAPTER 2 PCA-BASED TECHNIQUES In t
Page 27 and 28: The first step in a PCA analysis is
Page 29 and 30: of dimensions needed to represent t
Page 31 and 32: The PCA-SVD algorithm finds the val
Page 33 and 34: where Vr is a matrix with the first
Page 35 and 36: 2. Similarly combine the two segmen
Page 37 and 38: joints are known for every pose. He
Page 39 and 40: motion. Figure 3.1: Result of boole
Page 41 and 42: 2. Feature functions describing an
Page 43 and 44: F -equivalent if the corresponding
Page 45 and 46: 3.1.6.1 Inverted File Index Inverte
Page 47 and 48: the same set of exact hits. Figure
Page 49 and 50: CHAPTER 4 DYNAMIC TIME WARPING TECH
Page 51 and 52: Figure 4.1: Result of DTW technique
Page 53 and 54: operator \ to determine the relatio
Page 55 and 56:
zmin = ¯z + ¯x ′ · sin(θmin)
Page 57 and 58:
CHAPTER 5 EXPERIMENTS AND RESULTS W
Page 59 and 60:
5.2 Indexing Accuracy Testing To ev
Page 61 and 62:
Figure 5.3: False positives for PCA
Page 63 and 64:
Figure 5.5: False positives for PPC
Page 65 and 66:
Figure 5.7: False positives for Fea
Page 67 and 68:
Figure 5.9: False positives for DTW
Page 69 and 70:
Figure 5.11: False positives for DT
Page 71 and 72:
Figure 5.13: False Negatives detect
Page 73 and 74:
DTW[18]. DTW matching can also be i
Page 75 and 76:
Animation techniques can be broadly
Page 77 and 78:
directly modified or manipulated by
Page 79 and 80:
B.1 Breif History Human beings are
Page 81 and 82:
B.2 Motion Capture Technologies 1.
Page 83 and 84:
(b) Active markers: Markers have on
Page 85 and 86:
• Capture of movement: Subject we
Page 87 and 88:
animation abilities, it can be help
Page 89 and 90:
74 09 127 23 138 19 141 14 75 19 79
Page 91 and 92:
10 04 walk with hands moving more 0
Page 93 and 94:
118 05 jump 118 06 jump 118 07 jump
Page 95 and 96:
143 25 waiving 143 08 jumping twist
Page 97 and 98:
[9] A. J. Hanson, “Visualizing qu
Page 99:
BIOGRAPHICAL STATEMENT Harnish Bhat
show all

A COMPARISON AND EVALUATION OF MOTION INDEXING ...

Create successful ePaper yourself

Delete template?

Save as template?