Anomaly detection in videos has been attracting an increasing amount of attention. Despite
the competitive performance of recent methods on benchmark datasets, they typically lack …

Anomaly detection in surveillance videos is a challenging computer vision task where only
normal videos are available during training. Recent work released the first virtual anomaly …

Most video anomaly detection approaches are based on data-intensive end-to-end trained
neural networks, which extract spatiotemporal features from videos. The extracted feature …

Due to the limited availability of anomaly examples, video anomaly detection is often seen
as one-class classification (OCC) problem. A popular way to tackle this problem is by …

Automated detection of abnormalities in data has been studied in research area in recent
years because of its diverse applications in practice including video surveillance, industrial …

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 …

Progress in video anomaly detection research is currently slowed by small datasets that lack
a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move …

Video anomaly detection (VAD) aims at identifying unusual behaviors from videos. Most of
the existing video anomaly detection methods can achieve promising performance in the …

Anomaly detection in the video is an important research area and a challenging task in real
applications. Due to the unavailability of large-scale annotated anomaly events, most …

Video anomaly detection is of critical practical importance to a variety of real applications
because it allows human attention to be focused on events that are likely to be of interest, in …