Novel kernel-based recognizers of human actions

Authors:
Somayeh Danafar;Alessandro Giusti;Jürgen Schmidhuber
Affiliations:
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, University of Lugano, Manno-Lugano, Switzerland;Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, University of Lugano, Manno-Lugano, Switzerland;Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, University of Lugano, Manno-Lugano, Switzerland
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Year:
2010

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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 MaximumMean Discrepancy test statistic; and a supervised method based on Support Vector Machine with a characteristic kernel specifically tailored to histogram-based information.