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 …

Intelligent video anomaly detection (VAD) methods play a crucial role in conserving human
resources, reducing the financial burden on governments, and promptly and accurately …

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 …

Detecting anomalous events in videos is one of the most popular computer vision topics. It is
considered a challenging task in video analysis due to its definition, which is subjective or …

Video anomaly detection is a challenging task because of the uncertainty of abnormal
events. The current method based on predictive frames has obtained better detection results …

Video anomaly detection is a challenging task in the Computer vision community. Most
single task-based methods do not consider the independence of unique spatial and …

Anomaly detection in videos is commonly referred to as the discrimination of events that do
not conform to expected behaviors. Most existing methods formulate video anomaly …

The core component of most anomaly detectors is a self-supervised model, tasked with
modeling patterns included in training samples and detecting unexpected patterns as the …

Anomaly detection in video surveillance is challenging due to the variety of anomaly types
and definitions, which limit the use of supervised techniques. As such, auto-encoder …

Abnormal behavior detection in surveillance videos is necessary for public monitoring and
safety. In human-based surveillance systems, it requires continuous human attention and …