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, in an offline phase, defining at least one event as an n-dimensional feature vector extracted from training video data; in an offline phase, categorizing said events into said plurality of event classes according to said n-dimensional feature vector; using parametric representations of vehicle trajectories to define said n-dimensional feature vectors; in an offline phase, experimentally selecting at least one optimal nonlinear kernel function; in an offline phase, using said selected optimal non-linear kernel function to transform said n-dimensional feature vectors into a higher dimensional feature space by optimizing a kernel function parameterization; in an offline phase, setting a threshold indicative of whether or not a sample is anomalous; in an online phase, receiving at least one test event comprising a vehicle trajectory within an input video sequence of said video footage; in an online phase, 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 further comprising solving an optimization problem as follows: α ^ ′ = argmin α ⁢  α ′  1 subject ⁢ ⁢ to  ϕ ⁡ ( y ) - A ϕ ⁢ α ′  2 < ɛ wherein α′ comprises a sparse reconstruction coefficient vector, Φ(γ) comprises a transformation of a test event vector γ into said higher dimensional feature space, and A Φ comprises a dictionary representation in said higher dimensional feature space wherein said optimization problem is solved with a modified version of an Orthogonal Matching Pursuit algorithm; and in an online phase, determining if said at least one test event is anomalous according to said derived sparse reconstruction according to said threshold with respect to said training dictionary in said higher dimensional feature space induced by said at least one nonlinear kernel function.