1. A method for detecting anomalies in video footage, said method comprising: in an offline phase, constructing a training dictionary comprising a plurality of event classes, wherein at least one event among said plurality of event classes is defined with respect to n-dimensional feature vectors; in an offline phase, defining at least one nonlinear kernel function that transforms said n-dimensional feature vectors into a higher dimensional feature space; in an online phase, receiving at least one test event within an input video sequence of said video footage; and in an online phase, determining if said at least one test event is anomalous by deriving a sparse reconstruction with respect to said training dictionary in said higher dimensional feature space induced by said at least one nonlinear kernel function.