MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917619089539072 |
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| author | Micorek, Jakub Possegger, Horst Narnhofer, Dominik Bischof, Horst Kozinski, Mateusz |
| author_facet | Micorek, Jakub Possegger, Horst Narnhofer, Dominik Bischof, Horst Kozinski, Mateusz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection Micorek, Jakub Possegger, Horst Narnhofer, Dominik Bischof, Horst Kozinski, Mateusz Computer Vision and Pattern Recognition 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. |
| title | MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14497 |