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 …

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 …

Video anomaly detection has proved to be a challenging task owing to its unsupervised
training procedure and high spatio-temporal complexity existing in real-world scenarios. In …

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 …

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 …

Video anomaly detection is to automatically identify predefined anomalous contents (eg
abnormal objects, behaviors and scenes) in videos. The performance of video anomaly …

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 …

With the development of surveillance video analysis and the increase of security
requirements, video anomaly detection has attracted more and more attention. In the …

Unsupervised approaches for video anomaly detection may not perform as good as
supervised approaches. However, learning unknown types of anomalies using an …

Video anomaly detection is a challenging task owing to the rare and diverse nature of
abnormal events. However, most of the existing methods only learn the normality in a single …