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Human Action Recognition Week 10

Taylor Rassmann. Human Action Recognition Week 10. Overview Human Action Recognition. UCF50 dataset Begin with optical flow Lucas- Kanade with pyramids. Overview Human Action Recognition: Features. Kinematic Features Divergence Vorticity Symmetric Flow Fields Asymmetric Flow Fields.

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Human Action Recognition Week 10

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  1. Taylor Rassmann Human Action RecognitionWeek 10

  2. Overview Human Action Recognition • UCF50 dataset • Begin with optical flow • Lucas-Kanade with pyramids

  3. Overview Human Action Recognition: Features • Kinematic Features • Divergence • Vorticity • Symmetric Flow Fields • Asymmetric Flow Fields

  4. Overview Human Action Recognition: Learning • Method 1: • KernalPCA • Multiple Instance Learning • Method 2: • Bag of Words • Using built in K-Means with 500 centers

  5. Overview Human Action Recognition: Initial Results • Bag of Words: • 11 actions tested out of 50 • Accuracies ranged from 70-90% depending on the kinematic feature • Different features and approach necessary because of feature generation time

  6. Hierarchical SVMs • Look at a confusion matrix of the UCF50 dataset • Dollar Features • Method 1: • Find the least and most accurate classes • Method 2: • Find the two most confused classes • Train an SVM specifically on these two

  7. Hierarchical SVMs • Retrain and test with one less label than the previous iteration • Repeat for multiple levels 50 48 2

  8. Hierarchical SVMs: Method 1 Results • 25 levels deep after selection, training, and then testing • Different levels of the hierarchy produced different accuracies. • Initial Acc = 0.6989

  9. Hierarchical SVMs: Method 2 Results • 25 levels deep after selection, training, and then testing • Different levels of the hierarchy produced different accuracies. • Initial Acc = 0.7019

  10. Current Work • Automated action selection process finished • Analyze which actions are being grouped together as most confused • Method 2 results stayed consistently near the initial accuracy. • See if varying the number of classes per level changes the accuracy • Research different kinds of hierarchical structures

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