1. Exemplar-SVM for Action Recognition
Week 12
Presented by Christina Peterson

2. Recognition Accuracies on UCF Sports data set
Method
Accuracy (%)
Diving
Golfing
Kicking
Lifting
Riding
Running
Skating
Swing-bench
High-swing
Walking
Rodriguez et al. [1]
69.2 68 61 66 75 74 73 -
Yeffet and Wolf [2]
79.3
100 65 67 69 92 86
Le et al. [4]
86.5
77.8 80
66.7
83.3
90.9
Wu et al. [6]
91.3 88 93 84 95 91
Action Bank [7]
95.0 83 89
Standard Multiclass-SVMs
90.2 50
71.4
Combined Exemplar-SVMs
94.4
96.6
87.5
91.7
98.1
97.9
82.5

3. Confusion Matrix: Combined Exemplar-SVMDi Go Ki Li Ho Ru Sk Sb Ss Wa
Diving
96.6
1.9
1.5
87.5
8.3
4.2
91.7
100.0
83.3
16.7
98.1
97.9
2.1
2.8
94.4
3.3
10.9
82.5
Golf
Kick
Lift
Horse-Ride
Run
Skateboard
Swing-bench
Swing-side
Walk

4. Modifications Ran STIP for kicking action class
Lowered the threshold for weak interest points to obtain more interest points
The number of interest points collected for each video should be approximately equal to each other

5. Constraints The Exemplar Set The Validation Set/Test Set
Each exemplar should be a good representation of the action class
Needs a good variety
The Validation Set/Test Set
The validation set and test set should be similar to each other
Motion
Color
Confusing Videos were omitted from all sets
For example: Accelerating a skateboard by foot closely resembles walking

6. References
[1] M. D. Rodriguez, J. Ahmed, and M. Shah. Action mach: A spatio- temporal maximum average correlation height filter for action recognition. In CVPR, 2008.
[2] Yeffet and L. Wolf. Local trinary patterns for human action recognition. In ICCV, 2009.
[3] H. Wang, M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC,
[4] Q. Le, W. Zou, S. Yeung, and A. Ng. Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis. In CVPR, 2011.
[5] A. Kovashka and K. Grauman. Learning a hierarchy of discriminative spacetime neighborhood features for human action recognition. InCVPR, 2010.
[6] X. Wu, D. Xu, L. Duan, and J. Luo. Action recognition using context and appearance distribution features. InCVPR, 2011.
[7] S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. CVPR, 2012.