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Segmenting Motion Capture Data into Distinct Behaviors

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##### Segmenting Motion Capture Data into Distinct Behaviors

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**Segmenting Motion Capture Data into Distinct Behaviors**Graphics Interface ‘04 Speaker: Alvin January 17, 2005**Outline**• Introduction • Related Work • PCA • PPCA • GMM • Results • Conclusions Segmenting Motion Capture Data into Distinct Behaviors**Introduction**• Motion data are segmented at capture or by hand and are often small clips. • Longer shots contain natural transitions. • Segment motion into high-level behaviors. • Unsupervised Learning • Focus on efficient techniques: PCA, PPCA and GMM. Segmenting Motion Capture Data into Distinct Behaviors**Related Work**• Model-based Approach • Low-level • Detect zero crossings of angular velocities. • Motion texton • State Machine or Motion Graph • High-level • HMM • Clustering Segmenting Motion Capture Data into Distinct Behaviors**Goal**• Input: Motion data (14 motions, each 8000 frames) • FPS=120 • 14 Joints • Specify the rotation relative to the parent for all joints. • Rotations are specified by quaternions. • Output: Motion Clips • Automatically • Distinct Behaviors • Longer Segmenting Motion Capture Data into Distinct Behaviors**PCA**Center of motion: Approximation: SVD: Dimension: Projection Error: Derivative: Segmenting Motion Capture Data into Distinct Behaviors**PCA**Cut if di more than 3 standard deviations from the average Segmenting Motion Capture Data into Distinct Behaviors**Probabilistic PCA**• Average square of discard singular values: • Covariance Matrix: • Average Mahalanobis Distance • T=150, K=T • K:=K+△, △=10, Threshold R=15 Segmenting Motion Capture Data into Distinct Behaviors**PPCA**Segmenting Motion Capture Data into Distinct Behaviors**Gaussian Mixture Model**• Pre-processing: • Use PCA to project onto lower dimensional subspace. (Speed up EM) • Preserve 90% of the variance. • Each cluster is represented by a Gaussian Distribution. • EM • Estimate mean, covariance matrix, prior Segmenting Motion Capture Data into Distinct Behaviors**GMM**Segmenting Motion Capture Data into Distinct Behaviors**GMM**Cut if frames xi and xi+1 belong to different clusters Segmenting Motion Capture Data into Distinct Behaviors**Results**Error Matrix for PPCA Error Matrix for PCA Segmenting Motion Capture Data into Distinct Behaviors**Results**Segmenting Motion Capture Data into Distinct Behaviors**Results**Precision: Reported correct cuts / The total number of reported cuts Recall : Reported correct cuts / The total number of correct cuts Segmenting Motion Capture Data into Distinct Behaviors**論文簡報部份**完整性介紹(4) 系統性介紹(4) 表達能力(3) 投影片製作(3) 論文審閱部分 瞭解論文內容(4) 結果正確性與完整性 (4) 原創性與重要性(4) 讀後啟發與應用： Evaluation Form The mahalanobis distance can be adopted to my classification of motions. Besides, maybe I can exploit the GMM technique to classify for comparison. Segmenting Motion Capture Data into Distinct Behaviors**Conclusions**• Imperfect because observations’ opinions. • Treat all weights of DOF equally. • Each method require some parameters. • PCA-based methods work well. • ICA may achieve better cut detection. • No segmentation will apply for all applications. Segmenting Motion Capture Data into Distinct Behaviors**Mahalanobis Distance**• Dt(x) = (x–mt)S-1t(x–mt)' • Dt is the distance from t group • St represents the within-group covariance matrix • mt is the vector of the means of t group • X is the vector of frame values at location x • Superior to Euclidean distance because it takes distribution of the points (correlations) into account • Useful to determine the ”similarity” from an unknown sample to known samples • Classify observations into different groups Segmenting Motion Capture Data into Distinct Behaviors**GMM by Using EM**Segmenting Motion Capture Data into Distinct Behaviors