  Anomaly detection in videos is a problem that has been studied for more than
a decade. This area has piqued the interest of researchers due to its wide
applicability. Because of this, there has been a wide array of approaches that
have been proposed throughout the years and these approaches range from
statistical-based approaches to machine learning-based approaches. Numerous
surveys have already been conducted on this area but this paper focuses on
providing an overview on the recent advances in the field of anomaly detection
using Deep Learning. Deep Learning has been applied successfully in many fields
of artificial intelligence such as computer vision, natural language processing
and more. This survey, however, focuses on how Deep Learning has improved and
provided more insights to the area of video anomaly detection. This paper
provides a categorization of the different Deep Learning approaches with
respect to their objectives. Additionally, it also discusses the commonly used
datasets along with the common evaluation metrics. Afterwards, a discussion
synthesizing all of the recent approaches is made to provide direction and
possible areas for future research.
