1. Human Action Recognition Week 10
Taylor Rassmann
Human Action Recognition Week 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:
Kernal PCA
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 2 48

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
Level 1 Acc
0.6968
Level 2 Acc
0.6980
Level 3 Acc
0.6959
Level 4 Acc
0.6956
Level 5 Acc
0.6947 …
Level 25 Acc
0.6418

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
Level 1 Acc
0.6983
Level 2 Acc
0.6980
Level 3 Acc
0.6968
Level 4 Acc
0.6962
Level 5 Acc …
Level 25 Acc
0.6866

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