Margin Learning Embedded Prediction for Video Anomaly Detection
with A Few Anomalies
Abstract
Classical semi-supervised video anomaly detection
assumes that only normal data are available in the
training set because of the rare and unbounded nature of anomalies. It is obviously, however, these
infrequently observed abnormal events can actually help with the detection of identical or similar abnormal events, a line of thinking that motivates us to study open-set supervised anomaly
detection with only a few types of abnormal observed events and many normal events available.
Under the assumption that normal events can be
well predicted, we propose a Margin Learning Embedded Prediction (MLEP) framework. There are
three features in MLEP- based open-set supervised video anomaly detection: i) we customize
a video prediction framework that favors the prediction of normal events and distorts the prediction
of abnormal events; ii) The margin learning framework learns a more compact normal data distribution and enlarges the margin between normal and
abnormal events. Since abnormal events are unbounded, our framework consequently helps with
the detection of abnormal events, even for anomalies that have never been previously observed.
Therefore, our framework is suitable for the openset supervised anomaly detection setting; iii) our
framework can readily handle both frame-level and
video-level anomaly annotations. Considering that
video-level anomaly detection is more easily annotated in practice and that anomaly detection with
a few anomalies is a more practical setting, our
work thus pushes the application of anomaly detection towards real scenarios. Extensive experiments validate the effectiveness of our framework
for anomaly detection