Automatic anomaly detection plays a critical role in surveillance systems but requires
datasets comprising large amounts of annotated data to train and evaluate models …

Automatic anomaly detection for surveillance purposes has become an integral part of
accident prevention and early warning systems. The lack of sufficient real datasets for …

In the era of smart cities, the role of surveillance systems in ensuring community safety and
environmental sustainability has become increasingly significant. Traditional surveillance …

The results of anomaly detection are sensitive to the choice of detection algorithms as they
are specialized for different properties of data, especially for multidimensional data. Thus, it …

Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in
smart connected cities. Given the increasing rate of urbanization, the advancement of these …

Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. Timely detection of traffic violations and …

Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. In this paper, we present a survey on relevant …

Due to the rapid development of communication and sensing technology, a large amount of
mobile data is collected so that we can infer the complex movement laws of humans. For …

Data augmentation methods are commonly integrated into the training of anomaly detection
models. Previous approaches have primarily focused on replicating real-world anomalies or …

In response to the imbalance between normal and abnormal samples in existing anomaly
detection datasets, as well as the complexity in defining anomalies, we introduce a new …