1. A method for video anomaly detection based upon a sparsity model, said method comprising: computing at least two descriptors of an event from video acquired by at least one camera; combining said at least two descriptors to form an event matrix with respect to said event; performing a sparse reconstruction of said event matrix with respect to an over complete dictionary of training events represented by said at least two descriptors, said sparse reconstruction based on a simultaneous structured sparsity model; and determining if said event is anomalous based on an analysis of said sparse reconstruction by computing an outlier rejection measure with respect to said sparse reconstruction and utilizing said outlier rejection measure to determine if said event is anomalous.