VADiffusion: Compressed Domain Information Guided Conditional Diffusion for Video Anomaly Detection

The demand for security surveillance has grown exponentially, making video anomaly detection particularly crucial. Existing image-domain based anomaly detection algorithms face implementation challenges due to several drawbacks, including latency during long-distance transmission, the need for complete decoding, and the complexity of network inference structures. Moreover, current frame prediction methods using generative models suffer from low prediction quality and mode collapse. To tackle these challenges, we propose VADiffusion, a compressed domain information guided conditional diffusion framework. VADiffusion adopts a dual-branch structure that combines motion vector reconstruction and I-frame prediction, effectively addressing the limitations of the reconstruction method in identifying sudden anomalies and the struggles of the frame prediction method in detecting persistent anomalies. Furthermore, our proposed framework incorporates the diffusion model into the realm of video anomaly detection, thereby improving the stability and accuracy of the model. Specifically, we employ sparse sampling of the compressed video, utilizing I-frames to capture appearance information and motion vectors to represent motion-related details. Different from the existing independent two-branch mechanism, we adopt a reconstruction-assisted prediction strategy, leveraging I-frames and the reconstructed motion vectors from the reconstruction branch as conditions for the diffusion model utilized in frame prediction. Ultimately, we perform decision fusion of reconstruction and prediction branches to determine anomalies. Through extensive experiments, we demonstrate that our algorithm achieves an effective trade-off between detection accuracy and model complexity. The source code is publicly released at https://github.com/LHaoooo/VADiffusion.