Rule Augmentation and Perception Smoothing for Training-free Video Anomaly Detection with LLMs

Video Anomaly Detection VAD is widely applied in the field of public safe-ty. Recently, training-free video anomaly detection based on large language models LLMs has achieved remarkable progress. However, while pre-trained LLMs in previous methods contain rich general-domain knowledge, they often lack a nuanced understanding of domain-specific knowledge, leading to re-duced performance in specific scenarios, such as campus environments. Fur-thermore, these methods often overlook the temporal consistency and motion continuity between anomalous video frames when utilizing LLMs for score judgment. To address these challenges, we propose a method for video anomaly detection using rule augmentation and perception smoothing. Specifically, the rule augmentation strategy can automatically generate anomaly detection rules based on the management standards of various scenarios. Perception smoothing employs an adaptive temporal smoothing strategy to enhance the robustness of score judgment based on LLMs. Extensive experiments demonstrate that the proposed method not only outperforms state-of-the-art, training-free methods on general datasets such as UCF-Crime and XD-Violence, but also achieves significant improvements on the specific scenario dataset ShanghaiTech.