Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper
taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic …

Anomaly localization is an important problem in computer vision which involves localizing
anomalous regions within images with applications in industrial inspection, surveillance …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …

Unsupervised anomaly detection aims to build models to effectively detect unseen
anomalies by only training on the normal data. Although previous reconstruction-based …

Interest in automatic crowd behaviour analysis has grown considerably in the last few years.
Crowd behaviour analysis has become an integral part all over the world for ensuring …

In metropolitan environments, unmanned aerial vehicles (UAVs) equipped with video
surveillance equipment can monitor crowd behavior and maintain public safety. In high …

Human falls rarely occur; however, detecting falls is very important from the health and
safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …

Deep learning is increasingly used for unsupervised feature extraction and anomaly
detection in big datasets. Most deep learning based anomaly detection techniques …

We present a one-class anomaly detection method that uses time series sensor data to
detect anomalies or faults in semiconductor fabrication processes. Critically, this method is …