Visual Activity and Anomaly Detection

Visual anomaly detection identifies observations that deviate meaningfully from a model of normal behaviour. Because future anomaly classes cannot always be enumerated in advance, the problem is often framed as one-class learning or modelling the normal distribution.

Modelling normality

Anomaly detection may use autoencoders, density estimators, one-class classifiers or distances in learned embedding spaces. A new observation receives a score based on reconstruction error, likelihood or representation-space distance.

The decision threshold depends on operational cost. The same numerical score can imply a different decision when false alarms and missed events carry different consequences.

Spatial and temporal context

A surface defect on a production line is mainly spatial. Behaviour in surveillance video may be anomalous only because of motion, timing or location. Optical flow, tracking and temporal encoders are useful in such settings.

False alarms and distribution change

Rare-event systems are sensitive to base rate: even a small false-positive rate may generate many alarms. Precision-recall behaviour and event-level costs are therefore useful evaluation measures.

Distribution shift occurs when normal deployment data moves away from the training distribution because of camera changes, lighting, seasons or operating regime. Anomaly scores are most useful when interpreted together with that context.