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## Sparse Granger Causality Graphs for Human Action Classification

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**Sparse Granger Causality Graphs for Human Action**Classification Saehoon Yi and Vladimir Pavlovic Rutgers, The State University of New Jersey**Outline**• Objective and challenges • Previous work • Sparse Granger Causality Graph Model • Analysis and result • Conclusion**Objective**• Classify human action time series data Challenges • High dimensional time series data • Dimensionality reduction • Difficulties in interpretation • Idiosyncratic patterns of same action • Need to find commonality within an action**Previous work**• Learning dynamics of joints • Each action is modeled as Linear Dynamic System • C. Bregler, CVPR 97 • Align time series data • Dynamic Time Warping • Canonical Time Warping • F. Zhou and F. De la Torre, NIPS 2009 • Need to tune parameter for each pair of sequence • Isotonic Canonical Correlation Analysis • S. Shariat and V. Pavlovic, ICCV 2011**Our approach**• Robust representation of continuous joint movements using micro event point processes. • Models salient and sparse temporal relations among skeletal joints movements**Step 1: Generate micro event point processes**Continuous time series Joint angles on knees Detect maximal/minimal extreme points as events Micro event point processes**Step 2: Estimate Granger Causality Graph**• Granger causality in time • Given two AR time series X, Y • Granger causality**Granger causality in frequency**• Given two point processes , • Estimate power spectrum • Decompose spectrum using Wilson’s algorithm • Granger causality • [A. Nedungadi, G. Rangarajan, N. Jain, and M. Ding ’09]**Granger causality graph representation**• Estimate Granger causality • for each pair of micro events • f frequencies → summarized to 4 bands**Step 3: Learn L1 regularized regression**• Input : 16M2 Granger causality features • Output : action category label • Sparse regression coefficient W for each action • Common causality pattern within each class • Positive coefficient Wij • edge i→ j have high causality • Negative coefficient Wij • edge i→ j have low causality**Experiments**• HDM05 dataset • Motion capture sequence of 29 skeletal joints • Each action is performed by 5 subjects • 8 action classes are chosen**Experiment settings**• Two different cross validation settings • Cut 1 • Randomly partition training / testing across all subjects • Cut 2 • Test set subjects different from training subjects • To show classification accuracy on unseen data**Comparative result**Confusion matrix of SGCGM**Conclusion**• Learn common structure within an action • The sparse regression model chose which pairwise relationship is important for the action • Interpretability of the model • Granger causal graph describes temporal relationship between two joints**Thanks you.**Q & A