1. Exemplar-SVM for Action Recognition
Week 10
Presented by Christina Peterson

2. Movement Exemplar-SVMs
Tran and Torresani [1] based the MEX-SVM on the work of Malisiewicz et. al. [2]
Linear SVMs applied to histograms of space- time interest points (STIPs) calculated from sub-volumes of the video
Trained on one positive samples and many negative samples
Calibrate MEX-SVM’s using Platt’s Method

3. Overview of MEX-SVM

4. Results MEX-SVM Exemplar Accuracy Avg. Accuracy Catch 53.3 26.67
MEX-SVM
Exemplar
 Accuracy
Avg. Accuracy
Catch
53.3
26.67
Dribble
14.4
16.00
Ride Bike
25.6
34.00
Dive
24.4
21.33
Fencing
43.3
14.00
Golf
68.9
40.00
Ride Horse
36.7
54.00
Jump
28.9
24.00
Kick Ball
12.2
10.00
Walk
11.1
12.00

5. Results

6. Reasons for Discrepancies
Different training/testing set
MEX-SVMs trained on UCF50 data set, tested on HMDB51
Exemplar-SVM trained and tested on UCF50 data set
Exemplar Feature Vector
MEX-SVM used ground truth bounding box
Exemplar-SVM use entire video
Mid-Level Feature Vector
MEX-SVM Mid-Level Feature Dimension = Na x Ns x Np
Na = Number of Exemplars
Ns = Exemplar template scale
Np = Spatial-Temporal Pyramid Level
185 x 3 x ( ) = 40,515
Exemplar-SVM Mid-Level Feature Dimension = Na
Varied between 250 – 1,500

7. References
[1] D. Tran and L. Torresani. MEXSVMs: Mid-level Features for Scalable Action Recognition. Dartmouth Computer Science Techinical Report TR , January 2013.
[2] T. Malisiewicz, A. Gupta, and A. A. Efros. 
Ensemble of Exemplar SVMS for Object 
Detection and  Beyond. In Proc. ICCV, 2011.