1. E XEMPLAR -SVM FOR A CTION R ECOGNITION Week 11 Presented by Christina Peterson

2. C HANGES MADE TO C OMBINED E XEMPLAR -SVM S Multi-Class SVM trained on calibrated exemplar scores rather than raw exemplar-svm scores Ran STIP for Kicking action class to obtain descriptors for more frames

3. R ECOGNITION A CCURACIES ON UCF S PORTS DATA SET Combined Exemplar-SVMs increased from 67.3% accuracy to 75% accuracy Method Accuracy (%)DivingGolfingKickingLiftingRidingRunningSkating Swing- bench High- swingWalking Rodriguez et al. [1] 69.2686166 757473--- Yeffet and Wolf [2] 79.3100616567 6992--86 Le et al. [4] 86.510077.88010066.769.283.3100 90.9 Wu et al. [6] 91.310088100 679384959391 Action Bank [7] 95.0100 8310091921008986 Standard Multiclass-SVMs 77.3100 0 45100 25 Combined Exemplar-SVMs 75.11003917100 67100 28

4. C ONFUSION M ATRIX : C OMBINED E XEMPLAR -SVM 1 0.390.550.06 0.390.170.060.210.060.11 1 1 0.040.070.670.180.04 1 1 1 0.06 0.170.430.28 DiGoSsSbSkSk RuHoLiKiWaWa Diving Golf Kick Lift Horse-Ride Run Skateboar d Swing- bench Swing-side Walk

5. C ONCLUSIONS The changes have made performance comparable to Standard Multi-Class SVM The selected exemplar set has a large impact on the accuracy on the test set Improving accuracy would involve manually selecting the best exemplars to represent the action class

6. R EFERENCES [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, 2009. [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.