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