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.

通过使用预先训练的潜在扩散模型修复被屏蔽区域图像，并进一步通过混合来扰动光流以产生时间空间扭曲，提出了一种新的生成通用时空伪异常的方法。此外，通过学习重建质量、时间不规则性和语义不一致性等三种异常指标的简单统一框架，在开放集识别任务下检测现实世界的异常。在 Ped2、Avenue、ShanghaiTech 和 UBnormal 四个 VAD 基准数据集上进行了大量实验，证明我们的方法在 OCC 设置下的性能与其他现有的基于伪异常生成和重建的方法相当。同时我们的分析还检验了伪异常在这些数据集之间的可转移性和普适性，通过识别伪异常从而发现现实世界的异常提供了有价值的见解。