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 …

Most of the crowd abnormal event detection methods rely on complex hand-crafted features
to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have …

Deep learning-based video anomaly detection methods have drawn significant attention in
the past few years due to their superior performance. However, almost all the leading …

This work introduces a new approach to localize anomalies in surveillance video. The main
novelty is the idea of using a Siamese convolutional neural network (CNN) to learn a …

Anomaly detection is critically important for intelligent surveillance systems to detect in a
timely manner any malicious activities. Many video anomaly detection approaches using …

Abnormal crowd behavior detection is a hot research topic in the field of computer vision. In
order to solve the problems of high computational cost and the imbalance between positive …

The reliability of inferences made by data-driven systems hinges on the data's continued
conformance to the systems' initial settings and assumptions. When serving data (on which …

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote
sensing technology in present days, there has been a tremendous growth in the volume of …

Identifying unusual crowd events is highly challenging, laborious, and prone to errors in
video surveillance applications. We propose a novel end-to-end deep learning architecture …