Anomalous events detection in real-world video scenes is a challenging problem due to the
complexity of" anomaly" as well as the cluttered backgrounds, objects and motions in the …

In this paper we address the abnormality detection problem in crowded scenes. We propose
to use Generative Adversarial Nets (GANs), which are trained using normal frames and …

We present an efficient method for detecting anomalies in videos. Recent applications of
convolutional neural networks have shown promises of convolutional layers for object …

Modeling pedestrian dynamics and their implementation in a computer are challenging and
important issues in the knowledge areas of transportation and computer simulation. The aim …

Anomalous event detection is of utmost importance in intelligent video surveillance.
Currently, most approaches for the automatic analysis of complex video scenes typically rely …

We propose a novel framework for abnormal event detection in video that requires no
training sequences. Our framework is based on unmasking, a technique previously used for …

Computer Vision (CV) based surveillance in crowded scenes is one of the most significant
and promising topics due to the increase of people gatherings in public places where the …

This paper addresses the problem of joint detection and recounting of abnormal events in
videos. Recounting of abnormal events, ie, explaining why they are judged to be abnormal …

This paper proposes a fast and reliable method for anomaly detection and localization in
video data showing crowded scenes. Time-efficient anomaly localization is an ongoing …

Abnormal event detection in video surveillance is extremely important, especially for
crowded scenes. In recent years, many algorithms have been proposed based on hand …