文献类型：期刊文献

英文题名：Ensemble anomaly score for video anomaly detection using denoise diffusion model and motion filters

作者：Wang, Zhiqiang[1];Gu, Xiaojing[1];Hu, Jingyu[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2023

卷号：553

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20233214490207);WOS:【SCI-EXPANDED(收录号:WOS:001052301900001)】；

基金：This work is supported by the National Natural Science Foundation of China under Grant No. 61973122 and 61973120.

语种：英文

外文关键词：Video anomaly detection; Unsupervised learning; Object detection; Diffusion model; Domain generalization

摘要：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.