In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Surveillance cameras are increasingly being used worldwide due to the proliferation of
digital video capturing, storage, and processing technologies. However, the large volume of …

In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep
learning models have been proposed for medical image analysis for improved performance …

Unsupervised approaches for video anomaly detection may not perform as good as
supervised approaches. However, learning unknown types of anomalies using an …

Considering the increasing aging of the population, multi-device monitoring of the activities
of daily living (ADL) of older people becomes crucial to support independent living and early …

Abstract We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection a
novel video anomaly detection methodology that combines pseudo-anomalies dynamic …

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 …

Unmanned aerial vehicles assist in maritime search and rescue missions by flying over
large search areas to autonomously search for objects or people. Reliably detecting objects …

Abnormal event detection in videos is a challenging problem, partly due to the multiplicity of
abnormal patterns and the lack of their corresponding annotations. In this paper, we propose …

Video anomaly detection (VAD) is an essential but challenging task. Existing prevalent
methods focus on analyzing the reconstruction or prediction difference between normal and …