The current concept of smart cities influences urban planners and researchers to provide
modern, secured and sustainable infrastructure and gives a decent quality of life to its …

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 …

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 …

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …

Existing methods for anomaly detection based on memory-augmented autoencoder (AE)
have the following drawbacks:(1) Establishing a memory bank requires additional memory …

Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-
grained pseudo-labels based on weak-label and then self-training a classifier is currently a …

Anomaly detection is one of the most researched topics in computer vision and machine
learning. Manual detection of an oddity in a video costs significant time and money, so there …

This paper presents a novel attention-based adversarial autoencoder network (A3N) that
consists of a two-stream decoder to detect abnormal events in video sequences. The first …

With the fuzzy boundary between normal and abnormal video data, which cannot be well
distinguished by most methods, anomaly detection in video requires better characterization …

Crowd anomaly detection is one of the most popular topics in computer vision in the context
of smart cities. A plethora of deep learning methods have been proposed that generally …