Video anomaly detection is often seen as one-class classification (OCC)
problem due to the limited availability of anomaly examples. Typically, to
tackle this problem, an autoencoder (AE) is trained to reconstruct the input
with training set consisting only of normal data. At test time, the AE is then
expected to well reconstruct the normal data while poorly reconstructing the
anomalous data. However, several studies have shown that, even with only normal
data training, AEs can often start reconstructing anomalies as well which
depletes the anomaly detection performance. To mitigate this problem, we
propose a novel methodology to train AEs with the objective of reconstructing
only normal data, regardless of the input (i.e., normal or abnormal). Since no
real anomalies are available in the OCC settings, the training is assisted by
pseudo anomalies that are generated by manipulating normal data to simulate the
out-of-normal-data distribution. We additionally propose two ways to generate
pseudo anomalies: patch and skip frame based. Extensive experiments on three
challenging video anomaly datasets demonstrate the effectiveness of our method
in improving conventional AEs, achieving state-of-the-art performance.

本文提出了一种新的方法，通过训练自编码器（AE）模型重建正常数据，并使用生成的伪异常数据模拟数据分布，以有效改善基于 AE 的视频异常检测方法。在三个具有挑战性的视频异常检测数据集上的实验结果表明了该方法的有效性和优于传统方法的性能。