Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection has shown promise over alternate video anomaly detection methods in that the sparse representations exhibit excellent robustness under noise (common in surveillance videos) and missing or corrupted features, e.g. vehicle occlusion in transportation videos. One limitation of existing sparsity based video anomaly detection techniques is that they are based on only a single feature representation (known formally as video event encoding). One can easily envision that different event representations such as object trajectories and spatio-temporal volumes often contain correlated yet complementary information. In this paper, we propose to extend sparsity models based on single feature representations to simultaneous sparse representations of multiple feature representations. In this model, the matrix of sparse coefficients does not confirm to the commonly seen row-sparsity and a modified greedy heuristic approach that extends simultaneous orthogonal matching pursuit (SOMP) is needed to solve the resulting optimization problem. Experiments on two benchmark video datasets reveal that our method significantly outperforms state-of-The art approaches that utilize only a single-perspective or event encoding.