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

By Dilip Ravi Kiran, Mathew Thomas, Ranjith Parakkal. Journal of Imaging, 2018

484 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7198f45e979d4e7bb2ad2f8a5f098ab196c532b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “Finally, other AE variants that have been applied to AD include RNN-based AEs [194], [231], [397], [398], convolutional AEs [54], AE ensembles [126], [398], and variants that constrain the gradients [399] or actively control the latent code topology [400] of an AE.”
ADBench: Anomaly Detection Benchmark (2022, background): “…anomalies in real-world applications, e.g., a few anomalies identiﬁed by domain experts or human-in-the-loop techniques like active learning [5, 7, 78, 189] Notably, there is a group of semi-supervised AD algorithms [127, 128, 130, 131, 152, 168, 205] that have not been covered by existing…”
Deep Learning for Anomaly Detection: A Survey (2019, methodology): “Several works have studied the state-of-the-art deep models for video anomaly detection and have classiﬁed them based on the type of model and criteria of detection (Kiran et al. [2018], Chong and Tay [2015]).”
Progress in Outlier Detection Techniques: A Survey (2019, background): “Recently, more attention has been given to deep learning in many areas including several studies related to outlier detection problems [35], [36] [30], [207]–[209].”
Deep Semi-Supervised Anomaly Detection (2019, background): “Unlike other deep approaches (Ergen et al., 2017; Kiran et al., 2018; Min et al., 2018; Deecke et al., 2018; Golan & El-Yaniv, 2018), however, our Deep SAD method is not domain or data-type specific.”
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives (2021, methodology): “Currently, deep abnormality learning (DAD) has been used in various research topics, such as detecting fraudulent health-care transactions [41], identifying abnormalities in video streaming [58] and detecting credit card frauds [59].”
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019, background): “Anomaly detection is a major area of interest within the field of machine learning with various real-world applications spanning from biomedical [9] to video surveillance [10].”