A Survey on Deep Learning Techniques for Video Anomaly Detection

TL;DR

This survey reviews recent advances in deep learning techniques for video anomaly detection, categorizing approaches, datasets, and evaluation metrics, and discusses future research directions.

Contribution

It provides a comprehensive overview of deep learning methods for video anomaly detection, highlighting recent developments and categorizing approaches based on their objectives.

Findings

Deep learning has significantly improved video anomaly detection accuracy.

Various datasets and evaluation metrics are used in the field.

Future research directions include developing more robust and real-time methods.

Abstract

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.