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Automating the analysis of surveillance video footage is of great interestwhen urban environments or industrial sites are monitored by a large number ofcameras. As anomalies are often context-specific, it is hard to predefineevents of interest and collect labelled training data. A purely unsupervisedapproach for automated anomaly detection is much more suitable. For everycamera, a separate algorithm could then be deployed that learns over time abaseline model of appearance and motion related features of the objects withinthe camera viewport. Anything that deviates from this baseline is flagged as ananomaly for further analysis downstream. We propose a new neural networkarchitecture that learns the normal behavior in a purely unsupervised fashion.In contrast to previous work, we use latent code predictions as our anomalymetric. We show that this outperforms reconstruction-based and frameprediction-based methods on different benchmark datasets both in terms ofaccuracy and robustness against changing lighting and weather conditions. Bydecoupling an appearance and a motion model, our model can also process 16 to45 times more frames per second than related approaches which makes our modelsuitable for deploying on the camera itself or on other edge devices.

Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video