  In recent years, many works have addressed the problem of finding
never-seen-before anomalies in videos. Yet, most work has been focused on
detecting anomalous frames in surveillance videos taken from security cameras.
Meanwhile, the task of anomaly detection (AD) in videos exhibiting anomalous
mechanical behavior, has been mostly overlooked. Anomaly detection in such
videos is both of academic and practical interest, as they may enable automatic
detection of malfunctions in many manufacturing, maintenance, and real-life
settings. To assess the potential of the different approaches to detect such
anomalies, we evaluate two simple baseline approaches: (i) Temporal-pooled
image AD techniques. (ii) Density estimation of videos represented with
features pretrained for video-classification.
  Development of such methods calls for new benchmarks to allow evaluation of
different possible approaches. We introduce the Physical Anomalous Trajectory
or Motion (PHANTOM) dataset, which contains six different video classes. Each
class consists of normal and anomalous videos. The classes differ in the
presented phenomena, the normal class variability, and the kind of anomalies in
the videos. We also suggest an even harder benchmark where anomalous activities
should be spotted on highly variable scenes.
