  The current concept of Smart Cities influences urban planners and researchers
to provide modern, secured and sustainable infrastructure and give a decent
quality of life to its residents. To fulfill this need video surveillance
cameras have been deployed to enhance the safety and well-being of the
citizens. Despite technical developments in modern science, abnormal event
detection in surveillance video systems is challenging and requires exhaustive
human efforts. In this paper, we surveyed various methodologies developed to
detect anomalies in intelligent video surveillance. Firstly, we revisit the
surveys on anomaly detection in the last decade. We then present a systematic
categorization of methodologies developed for ease of understanding.
Considering the notion of anomaly depends on context, we identify different
objects-of-interest and publicly available datasets in anomaly detection. Since
anomaly detection is considered a time-critical application of computer vision,
our emphasis is on anomaly detection using edge devices and approaches
explicitly designed for them. Further, we discuss the challenges and
opportunities involved in anomaly detection at the edge.
