Flow-Guided Diffusion Autoencoder for Unsupervised Video Anomaly Detection

Aoni Zhu, Wenjun Wang, Cheng Yan

Abstract: Video anomaly detection (VAD) aims to automatically detect abnormalities that deviate from expected behaviors. Due to the heavy reliance of mainstream one-class methods on labeled normal samples, some unsupervised VAD methods have emerged. However, these methods are unable to detect both appearance and motion anomalies in videos comprehensively. To address the above problem, we present for the first time a Flow-guided Diffusion AutoEncoder (FDAE) that generates objects of each frame to detect anomalous in an unsupervised manner. Our model takes foreground objects and motion information as inputs to train a conditional diffusion autoencoder for foreground reconstruction. To make our model concentrate on learning normal samples, we further design a sample refinement scheme and introduce a mixed Gaussian clustering network to enhance the capability of the diffusion model in capturing typical characteristics of normal samples. Comprehensive experiments on three public available datasets demonstrate that the proposed FDAE outperforms all competing unsupervised approaches.

External IDs:dblp:conf/prcv/ZhuWY23