A Brief Survey on Contemporary Methods for Anomaly Detection in Videos

Abstract

Anomaly detection has stayed among the core topics of research in machine learning and computer vision for decades. Due to its complex nature, limitless possibilities of abnormal cases and the bounded amount of normal examples available, it is still among the popular topics of research. With the increase in surveillance cameras, it is becoming important to have systems which can learn from the vastly available normal data to identify any unusual happenings in the videos. In this paper, an attempt has been made to evaluate and compare the recently introduced such systems. In order to evaluate the methods for implementation as well as their possible applications, we have classified the major papers introduced in last 5 years by the network architectures and the datasets that are used to evaluate these methodologies.