1 / 10

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.

dot
Download Presentation

Human Action Recognition Week 10

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related