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

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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Video Anomaly Detection CHUK Avenue MULDE-object-centric-micro AUC 94.3% # 1
Anomaly Detection CUHK Avenue MULDE-object-centric-micro AUC 94.3% # 1
Video Anomaly Detection ShanghaiTech MULDE-object-centric-micro AUC 86.7% # 1
Anomaly Detection ShanghaiTech MULDE-frame-centric-micro AUC 81.3% # 15
Anomaly Detection ShanghaiTech MULDE-object-centric-micro AUC 86.7% # 2
Video Anomaly Detection ShanghaiTech MULDE-frame-centric-micro AUC 81.3% # 2
Anomaly Detection UBnormal MULDE-frame-centric-micro-one-class-classification AUC 72.8% # 2
Video Anomaly Detection UBnormal MULDE-frame-centric-micro-one-class-classification AUC 72.8% # 1
Video Anomaly Detection UCF-Crime MULDE-frame-centric-micro-one-class-classification AUC 78.5% # 1
Anomaly Detection UCF-Crime MULDE-frame-centric-micro-one-class-classification AUC 78.5% # 1
Anomaly Detection In Surveillance Videos UCF-Crime MULDE-frame-centric-micro-one-class-classification ROC AUC 78.5% # 12
Anomaly Detection UCSD Ped2 MULDE-object-centric-micro AUC 99.7% # 1
Video Anomaly Detection UCSD Ped2 MULDE-object-centric-micro AUC 99.7% # 1

Methods


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