Enabling Real-World Supervised Video Anomaly Detection: New Open-Set Benchmark and New Framework

The inherent unpredictability of abnormal events in real-world Video Anomaly Detection (VAD) presents significant challenges for model generalization. Early unsupervised methods aim to detect any anomalies as deviations from normal patterns, but they often equate rarity with abnormality, an assumption that leads to high false positives in open-world scenarios where uncommon behaviors are not necessarily abnormal. Current mainstream supervised approaches focus on memorizing discriminative features rather than learning the underlying pattern that governs normal and abnormal events. Although their performance on the seen anomalies is superior, their capability for robust open-set inference is still limited. Moreover, conventional closed-set evaluation benchmarks obscure this critical distinction by assuming identical anomaly types during training and testing. To address this limitation, this paper constructs the Real-world Supervised Open-set Benchmark (RSOB) built on a novel large-scale traffic abnormal event dataset with precise frame-level annotation, supporting evaluation across varying supervision granularities, from full to weak supervision settings. This benchmark is also the first to enable the real-world open-set evaluation constructed on the simulation of potential open-set scenarios in the application. Moreover, we further propose a human-prior-aware framework that learns domain-agnostic normality rules through a novel Human-Prior-Focused (HPF) feature space. This space is derived from a semantic-aware transformation on pre-trained feature space to effectively separate normal events while modeling generalizable human rules. Notably, the framework is architecture-agnostic and establishes the first unified solution applicable to both supervised and weakly supervised VAD paradigms. Our extensive experiments demonstrate the superiority of our framework over traditional discriminative methods across our benchmark as well as the conventional benchmark Ubnormal. The code and dataset will be publicly available.