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 Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …

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 …

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

Anomaly detection in video is a challenging computer vision problem. Due to the lack of
anomalous events at training time, anomaly detection requires the design of learning …

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 …

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

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

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 …