An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

TL;DR

This paper reviews deep learning methods for unsupervised and semi-supervised video anomaly detection, categorizing approaches and evaluating criteria to advance surveillance applications with limited annotations.

Contribution

It provides a comprehensive categorization and analysis of deep learning techniques for video anomaly detection, including evaluation criteria and simple comparative studies.

Findings

Deep learning methods vary based on model type and detection criteria.

Evaluation criteria for spatio-temporal anomaly detection are established.

Insights into the effectiveness of different approaches are discussed.

Abstract

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.