Abstract We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection a
novel video anomaly detection methodology that combines pseudo-anomalies dynamic …

Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …

Anomaly detection in videos remains a challenging task due to the ambiguous definition of
anomaly and the complexity of visual scenes from real video data. Different from the …

While classic video anomaly detection (VAD) requires labeled normal videos for training,
emerging unsupervised VAD (UVAD) aims to discover anomalies directly from fully …

Video anomaly detection research is generally evaluated on short isolated benchmark
videos only a few minutes long. However in real-world environments security cameras …

Anomaly detection in the video is an important research area and a challenging task in real
applications. Due to the unavailability of large-scale annotated anomaly events, most …

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 …

We propose a novel approach to video anomaly detection: we treat feature vectors extracted
from videos as realizations of a random variable with a fixed distribution and model this …

Video anomaly detection is to identify the abnormal objects, positions and behaviours during
the video sequences. It is an important but challenging problem in intelligent video …

Unsupervised anomaly detection in videos is a challenging task owing to the remarkable
generalization capacity of the deep convolutional autoencoders and the complex nature of …