Image/Video Deep Anomaly Detection: A Survey

TL;DR

This survey reviews recent deep learning methods for image and video anomaly detection, highlighting their challenges, limitations, and future research directions in the field.

Contribution

It provides an in-depth analysis of current deep learning approaches for visual anomaly detection and discusses existing challenges and future prospects.

Findings

Deep neural networks improve detection accuracy but are computationally intensive.

Current methods face challenges in real-world applicability and scalability.

Future research should address efficiency and robustness in anomaly detection.

Abstract

The considerable significance of Anomaly Detection (AD) problem has recently drawn the attention of many researchers. Consequently, the number of proposed methods in this research field has been increased steadily. AD strongly correlates with the important computer vision and image processing tasks such as image/video anomaly, irregularity and sudden event detection. More recently, Deep Neural Networks (DNNs) offer a high performance set of solutions, but at the expense of a heavy computational cost. However, there is a noticeable gap between the previously proposed methods and an applicable real-word approach. Regarding the raised concerns about AD as an ongoing challenging problem, notably in images and videos, the time has come to argue over the pitfalls and prospects of methods have attempted to deal with visual AD tasks. Hereupon, in this survey we intend to conduct an in-depth investigation into the images/videos deep learning based AD methods. We also discuss current challenges and future research directions thoroughly.