scalable salient motions detection from skeletal motion capture data samah ramadan
DESCRIPTION
Introduction Applications of human motion analysis Applications of human motion analysis Reconstruction of 3D models of humans Reconstruction of 3D models of humans Gait analysis and activity recognition Gait analysis and activity recognition Motion capture Motion capture Growing need for compact motion representation Growing need for compact motion representation Motion summarization Motion summarization Key frame extraction Key frame extractionTRANSCRIPT
Scalable Salient Motions Scalable Salient Motions Detection from Skeletal Detection from Skeletal
Motion Capture DataMotion Capture DataSamah RamadanSamah Ramadan
OutlineOutline IntroductionIntroduction Literature ReviewLiterature Review Singular Value DecompositionSingular Value Decomposition Salient Motion DetectionSalient Motion Detection ResultsResults ConclusionsConclusions
IntroductionIntroduction Applications of human motion analysisApplications of human motion analysis
Reconstruction of 3D models of humansReconstruction of 3D models of humans Gait analysis and activity recognitionGait analysis and activity recognition Motion captureMotion capture
Growing need for compact motion Growing need for compact motion representationrepresentation Motion summarizationMotion summarization Key frame extractionKey frame extraction
Literature ReviewLiterature Review Hand-drawn illustrations, Hand-drawn illustrations,
storyboards and comic booksstoryboards and comic books Speedlines, flow ribbons and opacity Speedlines, flow ribbons and opacity
modulationmodulation Dynamic Glyphs Dynamic Glyphs Streamline placementStreamline placement Curve simplificationCurve simplification Replicated multidimensional scalingReplicated multidimensional scaling
Singular Value Singular Value DecompositionDecomposition
Salient Motion DetectionSalient Motion Detection
Human Joint Configuration
Salient Motion DetectionSalient Motion Detection BVH FilesBVH Files
HIERARCHYROOT Hips{
OFFSET 0.00 0.00 0.00CHANNELS 6 Xposition Yposition Zposition Zrotation Xrotation YrotationJOINT Chest{
OFFSET 0.00 5.21 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT Neck{
OFFSET 0.00 18.65 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT Head{
OFFSET 0.00 5.45 0.00CHANNELS 3 Zrotation Xrotation YrotationEnd Site {
OFFSET 0.00 3.87 0.00}
}}JOINT LeftCollar{
OFFSET 1.12 16.23 1.87CHANNELS 3 Zrotation Xrotation YrotationJOINT LeftUpArm{
OFFSET 5.54 0.00 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT LeftLowArm{
OFFSET 0.00 -11.96 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT LeftHand{
OFFSET 0.00 -9.93 0.00CHANNELS 3 Zrotation Xrotation YrotationEnd Site {
OFFSET 0.00 -7.00 0.00}
}}
}}JOINT RightCollar{
OFFSET -1.12 16.23 1.87CHANNELS 3 Zrotation Xrotation YrotationJOINT RightUpArm{
OFFSET -6.07 0.00 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT RightLowArm{
OFFSET 0.00 -11.82 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT RightHand{
OFFSET 0.00 -10.65 0.00CHANNELS 3 Zrotation Xrotation YrotationEnd Site {
OFFSET 0.00 -7.00 0.00}
}}
}}
}JOINT LeftUpLeg{
OFFSET 3.91 0.00 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT LeftLowLeg{
OFFSET 0.00 -18.34 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT LeftFoot{
OFFSET 0.00 -17.37 0.00CHANNELS 3 Zrotation Xrotation YrotationEnd Site {
OFFSET 0.00 -3.46 0.00}
}}
}JOINT RightUpLeg{
OFFSET -3.91 0.00 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT RightLowLeg{
OFFSET 0.00 -17.63 0.00CHANNELS 3 Zrotation Xrotation YrotationJOINT RightFoot{
OFFSET 0.00 -17.14 0.00CHANNELS 3 Zrotation Xrotation YrotationEnd Site {
OFFSET 0.00 -3.75 0.00}
}}
}}MOTIONFrames: 2Frame Time: 0.033333 8.03 35.01 88.36 -3.41 14.78 -164.35 13.09 40.30 -24.60 7.88 43.80 0.00 -3.61 -41.45 5.82 10.08 0.00 10.21 97.95 -23.53 -2.14 -101.86 -80.77 -98.91 0.69 0.03 0.00 -14.04 0.00 -10.50 -85.52 -13.72 -102.93 61.91 -61.18 65.18 -1.57 0.69 0.02 15.00 22.78 -5.92 14.93 49.99 6.60 0.00 -1.14 0.00 -16.58 -10.51 -3.11 15.38 52.66 -21.80 0.00 -23.95 0.00 7.81 35.10 86.47 -3.78 12.94 -166.97 12.64 42.57 -22.34 7.67 43.61 0.00 -4.23 -41.41 4.89 19.10 0.00 4.16 93.12 -9.69 -9.43 132.67 -81.86 136.80 0.70 0.37 0.00 -8.62 0.00 -21.82 -87.31 -27.57 -100.09 56.17 -61.56 58.72 -1.63 0.95 0.03 13.16 15.44 -3.56 7.97 59.29 4.97 0.00 1.64 0.00 -17.18 -10.02 -3.08 13.56 53.38 -18.07 0.00 -25.93 0.00
Salient Motion DetectionSalient Motion Detection
Salient Motion DetectionSalient Motion Detection
Salient Motion DetectionSalient Motion Detection
Salient Motion DetectionSalient Motion Detection
Salient Motion DetectionSalient Motion Detection Sliding Window SVDSliding Window SVD
x i-N+1 x i-N+2 …….. x i x i+1 …….. x T
Window size N
Compute SVD and get the rank ri
Salient Motion DetectionSalient Motion Detection Sliding Window SVDSliding Window SVD
x i-N+1 xi-N+2 …….. x i x i+1 …….. x T
Window size N
Compute SVD and get the rank ri+1
Salient Motion DetectionSalient Motion Detection
Joint angels feature vectorJoint angels feature vector
Salient Motion DetectionSalient Motion Detection
Absolute location feature vectorAbsolute location feature vector
Salient Motion DetectionSalient Motion Detection
ResultsResults Code, paper and dataCode, paper and data
http://www.umiacs.umd.edu/http://www.umiacs.umd.edu/~sramadan/SalientMotionProject.htm~sramadan/SalientMotionProject.htm
ConclusionsConclusions Extracting salient motion is an Extracting salient motion is an
important problem in computer important problem in computer graphics, computer vision, etc.graphics, computer vision, etc.
SVD can be used to extract salient SVD can be used to extract salient motion framesmotion frames
Absolute position features are better Absolute position features are better than joint angle features in than joint angle features in representing the motion.representing the motion.
AcknowledgmentAcknowledgment Dr. Amitabh VarshneyDr. Amitabh Varshney Dr. Dianne O’LearyDr. Dianne O’Leary Youngmin KimYoungmin Kim