# A Survey on Deep Learning Techniques for Video Anomaly Detection

arXiv: https://arxiv.org/abs/2009.14146 · HTML: https://mlsanity.com/paper/2009.14146/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [Delving into CLIP latent space for Video Anomaly Recognition](https://mlsanity.com/paper/2310.02835/) (2023)
- [Deep Video Anomaly Detection: Opportunities and Challenges](https://mlsanity.com/paper/2110.05086/) (2021)
- [MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation](https://mlsanity.com/paper/2406.18815/) (2024)
- [Image/Video Deep Anomaly Detection: A Survey](https://mlsanity.com/paper/2103.01739/) (2021)
- [Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions](https://mlsanity.com/paper/2210.13927/) (2022)
- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [GraphVid: It Only Takes a Few Nodes to Understand a Video](https://mlsanity.com/paper/2207.01375/) (2022)
- [Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning](https://mlsanity.com/paper/2411.09072/) (2024)

## Abstract (arXiv)

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.