Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …

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 …

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 …

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent
surveillance system. However, an essential type of anomaly in VAD named scene …

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …

As technology continues to develop, computer vision (CV) applications are becoming
increasingly widespread in the intelligent transportation systems (ITS) context. These …

Detecting anomalies in videos is a fundamental issue in public security. The majority of
existing deep learning methods often perform anomaly detection based on the behavior or …

In contemporary society, surveillance anomaly detection, ie, spotting anomalous events
such as crimes or accidents in surveillance videos, is a critical task. As anomalies occur …

We present adversarial event prediction (AEP), a novel approach to detecting abnormal
events through an event prediction setting. Given normal event samples, AEP derives the …