This article summarizes research trends on the topic of anomaly detection in video feeds of a
single scene. We discuss the various problem formulations, publicly available datasets and …

Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. Timely detection of traffic violations and …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

Anomaly detection in video is a challenging computer vision problem. Due to the lack of
anomalous events at training time, anomaly detection requires the design of learning …

Detecting abnormal events in video is commonly framed as a one-class classification task,
where training videos contain only normal events, while test videos encompass both normal …

Anomaly detection in surveillance videos is currently a challenge because of the diversity of
possible events. We propose a deep convolutional neural network (CNN) that addresses …

Anomaly detection in videos refers to the identification of events that do not conform to
expected behavior. However, almost all existing methods tackle the problem by minimizing …

A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …