  Video Anomaly Detection (VAD) is an open-set recognition task, which is
usually formulated as a one-class classification (OCC) problem, where training
data is comprised of videos with normal instances while test data contains both
normal and anomalous instances. Recent works have investigated the creation of
pseudo-anomalies (PAs) using only the normal data and making strong assumptions
about real-world anomalies with regards to abnormality of objects and speed of
motion to inject prior information about anomalies in an autoencoder (AE) based
reconstruction model during training. This work proposes a novel method for
generating generic spatio-temporal PAs by inpainting a masked out region of an
image using a pre-trained Latent Diffusion Model and further perturbing the
optical flow using mixup to emulate spatio-temporal distortions in the data. In
addition, we present a simple unified framework to detect real-world anomalies
under the OCC setting by learning three types of anomaly indicators, namely
reconstruction quality, temporal irregularity and semantic inconsistency.
Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue,
ShanghaiTech and UBnormal demonstrate that our method performs on par with
other existing state-of-the-art PAs generation and reconstruction based methods
under the OCC setting. Our analysis also examines the transferability and
generalisation of PAs across these datasets, offering valuable insights by
identifying real-world anomalies through PAs.
