Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Video surveillance systems are popular and used in public places such as market places,
shop** malls, hospitals, banks, streets, education institutions, city administrative offices …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

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 …

Anomaly detection in videos refers to the identification of events that do not conform to
expected behavior. However, almost all existing methods tackle the problem by minimizing …

We address the problem of anomaly detection, that is, detecting anomalous events in a
video sequence. Anomaly detection methods based on convolutional neural networks …

Visual anomaly detection addresses the problem of classification or localization of regions in
an image that deviate from their normal appearance. A popular approach trains an auto …

Perceiving meaningful activities in a long video sequence is a challenging problem due to
ambiguous definition ofmeaningfulness' as well as clutters in the scene. We approach this …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

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 …