Video surveillance systems are used to enhance the public safety and private assets.
Automatic anomaly detection is vital in such surveillance systems to reduce the human labor …

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 …

Learning multiple pretext tasks is a popular approach to tackle the nonalignment problem in
unsupervised video anomaly detection. However, the conventional learning method of …

Our objective is to detect anomalies in video while also automatically explaining the reason
behind the detector's response. In a practical sense, explainability is crucial for this task as …

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 …

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 …

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 (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video.
Existing works mostly rely on training deep models to learn the distribution of normality with …