MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection

• URL: http://arxiv.org/abs/2403.14497v1
• Date: Thu, 21 Mar 2024 15:46:19 GMT
• Title: MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
• Authors: Jakub Micorek, Horst Possegger, Dominik Narnhofer, Horst Bischof, Mateusz Kozinski,
• Abstract summary: We treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution. We train our video anomaly detector using a modification of denoising score matching. Our experiments on five popular video anomaly detection benchmarks demonstrate state-of-the-art performance.
• Score: 15.72443573134312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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