Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

The task of trajectory outlier detection is to discover trajectories or their segments which
differ substantially from or are inconsistent with the remaining set. In this paper, we make an …

Anomaly detection in surveillance videos is currently a challenge because of the diversity of
possible events. We propose a deep convolutional neural network (CNN) that addresses …

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we
propose to learn anomalies by exploiting both normal and anomalous videos. To avoid …

Novelty detection is commonly referred as the discrimination of observations that do not
conform to a learned model of regularity. Despite its importance in different application …

A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …

As a vital topic in media content interpretation, video anomaly detection (VAD) has made
fruitful progress via deep neural network (DNN). However, existing methods usually follow a …

Learning to detect real-world anomalous events through video-level labels is a challenging
task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we …