Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Video surveillance systems are popular and used in public places such as market places,
shopping malls, hospitals, banks, streets, education institutions, city administrative offices …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

This article proposes an unsupervised deep learning–based approach to detect structural
damage. Supervised deep learning methods have been proposed in recent years, but they …

Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular
method for video anomaly detection. With models trained on the normal data, the …

We propose a one-class neural network (OC-NN) model to detect anomalies in complex
data sets. OC-NN combines the ability of deep networks to extract a progressively rich …

Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

We address the problem of anomaly detection in videos. The goal is to identify unusual
behaviours automatically by learning exclusively from normal videos. Most existing …