  Video anomaly detection (VAD) is a challenging computer vision task with many
practical applications. As anomalies are inherently ambiguous, it is essential
for users to understand the reasoning behind a system's decision in order to
determine if the rationale is sound. In this paper, we propose a simple but
highly effective method that pushes the boundaries of VAD accuracy and
interpretability using attribute-based representations. Our method represents
every object by its velocity and pose. The anomaly scores are computed using a
density-based approach. Surprisingly, we find that this simple representation
is sufficient to achieve state-of-the-art performance in ShanghaiTech, the
largest and most complex VAD dataset. Combining our interpretable
attribute-based representations with implicit, deep representation yields
state-of-the-art performance with a $99.1\%, 93.3\%$, and $85.9\%$ AUROC on
Ped2, Avenue, and ShanghaiTech, respectively. Our method is accurate,
interpretable, and easy to implement.
