A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.

Unsupervised video anomaly detection aims to learn normal spatio-temporal patterns from unlabeled surveillance data and identify deviations during inference. Existing methods often struggle to capture long-term temporal dependencies and adapt to spatial variations caused by target deformation and viewpoint changes. Although diffusion-based models provide stable distribution modeling, their limited temporal conditioning and fixed spatial representations restrict anomaly discrimination in complex scenes. To address these issues, we propose a boundary regularization enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion. Rather than relying on rigid feature extraction, our method explicitly models long-range motion evolution and dynamically adapts to spatial variations caused by target deformation and viewpoint shifts. Furthermore, to prevent overfitting to normal behaviors, we introduce a hybrid pseudo-anomaly generation strategy that acts as a boundary regularization constraint, explicitly sharpening the model's discriminative capability against abnormal patterns. Experiments on benchmark datasets including UCSD Ped2, CUHK Avenue, and ShanghaiTech demonstrate that our method significantly improves detection accuracy. By effectively integrating context-adaptive conditioning and boundary regularization, this work establishes a highly discriminative and generalizable paradigm for video anomaly detection, demonstrating strong robustness in complex, real-world surveillance scenarios.