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Week 9 Presented by Christina Peterson
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Recognition Accuracies on UCF Sports data set 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-SVMs77.3100 0 45100 25 Combined Exemplar-SVMs67.31007710100 332810025
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Confusion Matrix: Standard Multi-Class SVM 1 1 0.50 1 1 0.450.55 1 1 1 0.750.25 DiGoSsSbSkRuHoLiKiWa Diving Golf Kick Lift Horse-Ride Run Skateboard Swing-bench Swing-side Walk
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Confusion Matrix: Combined Exemplar-SVM 1 0.770.22 0.10.30.6 1 1 1 0.33 0.280.72 1 0.50.25 DiGoSsSbSkRuHoLiKiWa Diving Golf Kick Lift Horse-Ride Run Skateboard Swing-bench Swing-side Walk
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Standard Linear vs. Exemplar
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Conclusions Need to improve the method used to combine the exemplar scores Currently, a multiclass-svm is trained on the decision values of the exemplars on the validation set Possible Solution: Create a strong action classifier using the boosting algorithm of Viola and Jones[8] Treat the exemplar-svms as weak classifiers
![Conclusions Need to improve the method used to combine the exemplar scores Currently, a multiclass-svm is trained on the decision values of the exemplars on the validation set Possible Solution: Create a strong action classifier using the boosting algorithm of Viola and Jones[8] Treat the exemplar-svms as weak classifiers](http://images.slideplayer.com./25/8028002/slides/slide_6.jpg "Conclusions Need to improve the method used to combine the exemplar scores Currently, a multiclass-svm is trained on the decision values of the exemplars on the validation set Possible Solution: Create a strong action classifier using the boosting algorithm of Viola and Jones[8] Treat the exemplar-svms as weak classifiers")
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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, 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. [8] P. Viola and M. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, May 2004.
![References [1] M. D. Rodriguez, J. Ahmed, and M.](http://images.slideplayer.com./25/8028002/slides/slide_7.jpg "Shah. Action mach: A spatio-temporal maximum average correlation height filter for action recognition. In CVPR, [2] Yeffet and L. Wolf. Local trinary patterns for human action recognition. In ICCV, [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, [5] A. Kovashka and K. Grauman. Learning a hierarchy of discriminative spacetime neighborhood features for human action recognition. InCVPR, [6] X. Wu, D. Xu, L. Duan, and J. Luo. Action recognition using context and appearance distribution features. InCVPR, [7] S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. CVPR, [8] P. Viola and M. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, May")
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