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We address an anomaly detection setting in which training sequences areunavailable and anomalies are scored independently of temporal ordering.Current algorithms in anomaly detection are based on the classical densityestimation approach of learning high-dimensional models and findinglow-probability events. These algorithms are sensitive to the order in whichanomalies appear and require either training data or early context assumptionsthat do not hold for longer, more complex videos. By defining anomalies asexamples that can be distinguished from other examples in the same video, ourdefinition inspires a shift in approaches from classical density estimation tosimple discriminative learning. Our contributions include a novel framework foranomaly detection that is (1) independent of temporal ordering of anomalies,and (2) unsupervised, requiring no separate training sequences. We show thatour algorithm can achieve state-of-the-art results even when we adjust thesetting by removing training sequences from standard datasets.

A Discriminative Framework for Anomaly Detection in Large Videos