A COMPARISON AND EVALUATION OF MOTION INDEXING ...

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3.1.6 Indexing and Querying of Database . . . . . . . . . . . . . . . 34 4. DYNAMIC TIME WARPING TECHNIQUES . . . . . . . . . . . . . . . . 39 4.1 Scaling and Thresholding the DTW Score . . . . . . . . . . . . . . . 42 4.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 3-D Point Cloud based Distance: . . . . . . . . . . . . . . . . 43 4.2.2 Quaternion-based Distance: . . . . . . . . . . . . . . . . . . . 45 5. EXPERIMENTS AND RESULTS . . . . . . . . . . . . . . . . . . . . . . . 47 5.1 Performance Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.2 Indexing Accuracy Testing . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.1 Overall Results . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6. CONCLUSION AND FUTURE WORK . . . . . . . . . . . . . . . . . . . 62 APPENDIX A. ANIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 B. MOTION CAPTURE TECHNOLOGY . . . . . . . . . . . . . . . . . . . . 68 C. LIST OF MOCAP FILES USED FOR TESTING . . . . . . . . . . . . . . 78 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 BIOGRAPHICAL STATEMENT . . . . . . . . . . . . . . . . . . . . . . . . . 89 viii
LIST OF FIGURES Figure Page 1.1 Skeleton with bone names and id. . . . . . . . . . . . . . . . . . . . . . 11 1.2 Hierarchy in a sample ASF File. . . . . . . . . . . . . . . . . . . . . . 12 1.3 Part of a sample ASF File. . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Snapshot of a sample AMC File. . . . . . . . . . . . . . . . . . . . . . 13 3.1 Result of boolean feature function. . . . . . . . . . . . . . . . . . . . . 29 3.2 Process of Indexing Document. . . . . . . . . . . . . . . . . . . . . . . 36 3.3 Process of computing Exact Hits. . . . . . . . . . . . . . . . . . . . . . 37 4.1 Result of DTW technique on 2 similar pick queries. . . . . . . . . . . . 41 5.1 Performance of five techniques for 100 mocap file computations. . . . . 48 5.2 False negatives for PCA-SVD technique. . . . . . . . . . . . . . . . . . 50 5.3 False positives for PCA-SVD technique. . . . . . . . . . . . . . . . . . 51 5.4 False negatives for PPCA technique. . . . . . . . . . . . . . . . . . . . 52 5.5 False positives for PPCA technique. . . . . . . . . . . . . . . . . . . . 53 5.6 False negatives for Feature-based technique. . . . . . . . . . . . . . . . 54 5.7 False positives for Feature-based technique. . . . . . . . . . . . . . . . 55 5.8 False negatives for DTW-Quat technique. . . . . . . . . . . . . . . . . 56 5.9 False positives for DTW-Quat technique. . . . . . . . . . . . . . . . . 57 5.10 False negatives for DTW-3D technique. . . . . . . . . . . . . . . . . . 58 5.11 False positives for DTW-3D technique. . . . . . . . . . . . . . . . . . . 59 5.12 False Positives detected by each Technique. . . . . . . . . . . . . . . . 60 5.13 False Negatives detected by each Technique. . . . . . . . . . . . . . . . 61 ix
Page 1 and 2: A COMPARISON AND EVALUATION OF MOTI
Page 3 and 4: To my mom, dad, brother, friends an
Page 5 and 6: ABSTRACT A COMPARISON AND EVALUATIO
Page 7: TABLE OF CONTENTS ACKNOWLEDGEMENTS
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
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Figure 5.13: False Negatives detect
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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
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• 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
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118 05 jump 118 06 jump 118 07 jump
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143 25 waiving 143 08 jumping twist
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[9] A. J. Hanson, “Visualizing qu
Page 99:
BIOGRAPHICAL STATEMENT Harnish Bhat
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