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Data Driven Attributes for Action Recognition

Data Driven Attributes for Action Recognition. Week 8 Presented by Christina Peterson. Combined Scores of Exemplar-SVMs. Use decision values of Exemplar-SVM’s on validation set to train a Multiclass-SVM

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Data Driven Attributes for Action Recognition

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  1. Data Driven Attributes for Action Recognition Week 8 Presented by Christina Peterson

  2. Combined Scores of Exemplar-SVMs • Use decision values of Exemplar-SVM’s on validation set to train a Multiclass-SVM • If feature vector of validation set overlaps ground truth by 0.5 or higher, set the label to the respective action class

  3. Combined Scores of Exemplar-SVMs Label Action 1 Ex-2 Action 2 Ex-2 Action 2 Ex-4 Action 3 Ex-1 Action 1 Ex-1 Action 1 Ex-3 Action 1 Ex-4 Action 2 Ex-1 Action 2 Ex-3 Action 2 Ex-2 1 10 Exemplar-SVM Decision Values

  4. Preliminary Results • Multiclass-SVM achieved 64.29% accuracy • 18/28 classifications

  5. Confusion Matrix Di Go Ki Li Ho Ru Sk Sb Ss Wa Diving Golf Kick Lift Horse-Ride Run Skateboard Swing-bench Swing-side Walk

  6. Conclusions • Exemplars for Golf, Skateboard, Swing-bench, and Walk need to be checked • Validation Set is small • Currently consists of 2 videos from each action class (20 videos total) • Retrain Multiclass-SVM based on larger validation set

  7. References [1] T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of exemplar-SVMs for object detection and beyond. ICCV, 2011. [2] D. Tran and L. Torresani. MEXSVMs: Mid-level Features for Scalable Action Recognition. Dartmouth Computer Science Technical Report TR2013-726, January 2013.

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