Ensemble anomaly score for video anomaly detection using denoise diffusion model and motion filters

Video anomaly detection is a crucial task that aims to differentiate between normal and abnormal events. The current mainstream approach involves constructing an anomaly score based on the reconstruction error from a prediction model trained on normal frame sequences. However, this approach is limited by its deterministic nature, which may cause the anomaly score to be sensitive to underlying noise in the video. To address this limitation, this paper proposes an ensemble anomaly score constructed using a series of stochastic reconstructions of the original prediction. Specifically, we introduce the denoise diffusion model as a perturbation-denoise tool. First, the original prediction undergoes a perturbation process through a diffusion process. Then, a denoise diffusion model trained on normal predictions is used to directly reconstruct a series of noise-free predictions from the perturbed versions with different noise levels. Finally, an ensemble of all the reconstruction errors is used to provide a more generic and regularized anomaly score. Furthermore, we introduce motion filters into the detection pipeline to improve the modeling accuracy of the image distribution. The proposed method is evaluated on public datasets, and experimental results demonstrate its effectiveness, particularly in detecting performance under out-of-distribution (OOD) conditions.