Video Anomaly Detection Beyond AUC: A Protocol-Aware Evidence Review of Methods, Benchmark Protocols, Reproducibility, and Deployment Readiness

Video anomaly detection (VAD) now spans normality modelling, weak supervision, multimodal and vision-language approaches, training-free inference, and video anomaly understanding (VAU). This diversity has made simple cross-paper ranking increasingly fragile because identical benchmark names can conceal differences in supervision, feature extraction, preprocessing, evaluation population, aggregation, post-processing, and model selection. This protocol-aware evidence review therefore treats comparability as a prerequisite for performance ranking. A bounded core corpus of 193 peer-reviewed VAD/VAU studies published from 2015 to 14 October 2026 was assembled through a documented structured-seed and official-source update process, with source-specific verification against major publisher and venue records. All core records received bibliographic, methodological-regime, provenance, verification, and public-code-link coding, while 12 studies underwent an extended primary-source protocol audit. The Protocol Compatibility Framework is demonstrated with study-level Tier A, B, and C cases. For weakly supervised UCF-Crime studies using I3D-RGB features, the audit separated preprocessing-specific comparisons: MIST and MSL formed a one-crop Tier-A pair (82.30% and 85.30% AUC), while RTFM, UR-DMU, and MGFN formed a ten-crop Tier-A stratum (84.30%–86.98%; median 86.97%). S3R was excluded from the strict quantitative summary because its crop setting was not explicit in the original S3R publication. Public code links were directly identifiable for 79 of 193 core records (40.9%), reported descriptively rather than as a population estimate. The review also introduces a worked Minimum VAD Reporting Standard (MVRS). Together, the framework and evidence register support more defensible benchmark synthesis by making protocol compatibility, traceability, reproducibility, and deployment context explicit before numerical claims are compared.

Keywords

video anomaly detection, video anomaly understanding, benchmark protocols, reproducibility, deployment readiness.

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Video Anomaly Detection Beyond AUC: A Protocol-Aware Evidence Review of Methods, Benchmark Protocols, Reproducibility, and Deployment Readiness

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