Recent research has shown that features obtained from pretrained Convolutional Neural
Network (CNN) models can be promptly applied to a variety of problems they were not …

Accounting for the increased concern for public safety, automatic abnormal event detection
and recognition in a surveillance scene is crucial. It is a current open study subject because …

Surveillance cameras are being installed in many primary daily living places to maintain
public safety. In this video-surveillance context, anomalies occur only for a very short time …

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search,
and rescue operations by the virtue of low-cost, large-coverage, real-time, and high …

Abstract Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events
from video data where both known anomalies and novel ones exist in testing. Unsupervised …

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 …

Video Surveillance Systems (VSSs) are used in a wide range of applications including
public safety and perimeter security. They are deployed in places such as markets …

Detecting anomalies in surveillance videos has long been an important but unsolved
problem. In particular, many existing solutions are overly sensitive to (often ephemeral) …

In current technological era, surveillance systems generate an enormous volume of video
data on a daily basis, making its analysis a difficult task for computer vision experts …

The increasing popularity of compact and inexpensive cameras, eg dash cameras, body
cameras, and cameras equipped on robots, has sparked a growing interest in detecting …