A COMPARISON AND EVALUATION OF MOTION INDEXING ...

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Table 3.1: List of Feature Functions used to create Document. Function joint1 joint2 joint3 joint4 threshold Right leg ahead root ltibia lhipjoint rtoes NA Left leg ahead root rtibia rhipjoint ltoes NA Right leg sideways lhipjoint rhipjoint rhipjoint rtibia 1.2*hip width Left leg sideways rhipjoint lhipjoint lhipjoint ltibia 1.2*hip width Right knee bent rfemur rhipjoint rfemur rtibia 110 Left knee bent lfemur lhipjoint lfemur lrtibia 110 Right foot raised [0,0,0] [0,1,0] [0,Ymin,0] rtibia 1.2*right humerus length Left foot raised [0,0,0] [0,1,0] [0,Ymin,0] ltibia 1.2*left humerus length Right hand reach- root lclavicle rclavicle rwrist right humerus ing out to the front length Left hand reaching root rclavicle lclavicle lwrist left humerus out to the front Right hand above neck Left hand above neck length thorax lowerneck lowerneck rwrist 0.2*right humerus length thorax lowerneck lowerneck lwrist 0.2*left humerus length Right elbow bent rhumerus rclavicle rhumerus rwrist 110 Left elbow bent lhumerus lclavicle lhumerus lwrist 110 Hands far apart lclavicle rclavicle lwrist rwrist 2.5*shoulder sideways width Right hand lowered [0,0,0] [0,-1,0] [0,Ymin,0] rwrist -1.2*right humerus length Left hand lowered [0,0,0] [0,-1,0] [0,Ymin,0] lwrist -1.2*left humerus length Root behind frontal rtibia upperneck ltibia root 0.5*right plane humerus length Spine horizontal upperneck root [0,0,0] [0,1,0] 110 38
CHAPTER 4 DYNAMIC TIME WARPING TECHNIQUES This chapter provides a brief overview of Dynamic Time Warping (DTW) and the process of threshold selection. The threshold plays an important role in determin- ing the matches in the algorithm. The latter part of chapter describes two techniques to index motion data using Dynamic Time Warping. Both these techniques have been refered from the book, Information retrieval for music and motion [16]. The Dynamic Time Warping algorithm can be explained as an algorithm that calculates an optimal warping path to correspond two time series. The algorithm calculates both warping path values between the two series and the distance between them using dynamic programming. Formally, consider two numerical sequences (a1, a2, · · · , an) and (b1, b2, · · · , bm) which are of different length (n = m). The algorithm starts with the calculation of local distances between the elements of the two sequences. These distances can be of different types depending on the data and appli- cation. Euclidian distance is the most commonly used distance. The local distances are stored in a distance matrix D which has n rows and m columns. Each element of the matrix Dis given by: dij =| ai − bj |, i = {1, n} , j = {1, m} . For i = 1, · · · · · · , n and j = 1, · · · · · · , m, where all denotes a local distance. The next step in the DTW algorithm is to determine the matching cost matrix C. This is performed using the local distances matrix D and by computing the minimal distance 39
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 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: the same set of exact hits. Figure
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
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