Sparse representation based anomaly detection with enhanced local dictionaries

In this paper, we propose a novel approach for anomaly detection by modeling the usual behaviour with enhanced dictionary. The corresponding sparse reconstruction error indicates the anomaly. We compute the dictionaries, for each local region, from feature descriptors obtained from usual behavior. The novelty of the proposed work is in enhancing the local dictionaries based on the similarity of usual behavior with its spatial neighbors. Dictionary enhancement is achieved by appending `transformed dictionary' to the `local dictionary'. This `transformed dictionary' is learned based on the transformations of behavior patterns across two neighboring regions. We conduct experiments on widely used UCSD Ped1 and Ped2 datasets to compare with the existing algorithms and demonstrate the improvement in anomaly detection with enhanced dictionaries compared to typically learned local dictionary.