Novel Kernel-Based Recognizers of Human Actions. (16th May 2010) - Danafar Danafar, Somayeh Somayeh; Giusti Giusti, Alessandro Alessandro; Schmidhuber Schmidhuber, Jürgen Jürgen
Abstract : We study unsupervised and supervised recognition of human actions in video sequences. The videos are represented by probability distributions and then meaningfully compared in a probabilistic framework. We introduce two novel approaches outperforming state-of-the-art algorithms when tested on the KTH and Weizmann public datasets: an unsupervised nonparametric kernel-based method exploiting the Maximum Mean Discrepancy test statistic; and a supervised method based on Support Vector Machine with a characteristic kernel specifically tailored to histogram-based information.