INRIA-WILLOW at TRECVid 2010: Surveillance Event Detection
Rachid Benmokhtar, Ivan Laptev · 2010
Abstract. This notebook paper presents a system evaluated in the Surveillance Event Detection (SED) task of TRECVid 2010 campaign. We investigate a generic statistical approach applied to seven event classes defined by the SED task. Our video representation is based on local space-time descriptors which are vectorquantized and aggregated into histograms within short temporal windows and spatial regions defined by the prior. We use priors on the spatial localization of actions estimated from the spatio-temporal annotation of actions in the training data. To recognize actions, we learn one-against-all action classifiers using nonlinear SVMs. Each classifier is applied independently to localize temporal intervals of actions using window-scanning approach. We present results of six runs with variations in the two parameters: (i) classifier threshold and (ii) temporal extent of the scanning window. 1