Street Scene: A new dataset and evaluation protocol for video anomaly detection

Michael Jeffrey Jones, Bharathkumar Ramachandra

Abstract:

Progress in video anomaly detection research is currently
slowed by small datasets that lack a wide variety of
activities as well as flawed evaluation criteria. This paper
aims to help move this research effort forward by introducing
a large and varied new dataset called Street Scene, as
well as two new evaluation criteria that provide a better estimate
of how an algorithm will perform in practice. In addition
to the new dataset and evaluation criteria, we present
two variations of a novel baseline video anomaly detection
algorithm and show they are much more accurate on Street
Scene than two well known algorithms from the literature.