  This paper addresses video anomaly detection problem for videosurveillance.
Due to the inherent rarity and heterogeneity of abnormal events, the problem is
viewed as a normality modeling strategy, in which our model learns
object-centric normal patterns without seeing anomalous samples during
training. The main contributions consist in coupling pretrained object-level
action features prototypes with a cosine distance-based anomaly estimation
function, therefore extending previous methods by introducing additional
constraints to the mainstream reconstruction-based strategy. Our framework
leverages both appearance and motion information to learn object-level behavior
and captures prototypical patterns within a memory module. Experiments on
several well-known datasets demonstrate the effectiveness of our method as it
outperforms current state-of-the-art on most relevant spatio-temporal
evaluation metrics.
