Action recognition using Partial Least Squares and Support Vector Machines

We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied our approach to INRIA's IXMAS dataset. Experimental results show that our method is superior to other methods applied to the IXMAS dataset.