  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 distribution with a neural network. This lets us
estimate the likelihood of test videos and detect video anomalies by
thresholding the likelihood estimates. We train our video anomaly detector
using a modification of denoising score matching, a method that injects
training data with noise to facilitate modeling its distribution. To eliminate
hyperparameter selection, we model the distribution of noisy video features
across a range of noise levels and introduce a regularizer that tends to align
the models for different levels of noise. At test time, we combine anomaly
indications at multiple noise scales with a Gaussian mixture model. Running our
video anomaly detector induces minimal delays as inference requires merely
extracting the features and forward-propagating them through a shallow neural
network and a Gaussian mixture model. Our experiments on five popular video
anomaly detection benchmarks demonstrate state-of-the-art performance, both in
the object-centric and in the frame-centric setup.
