Most existing video anomaly detectors rely solely on RGB frames, which lack
the temporal resolution needed to capture abrupt or transient motion cues, key
indicators of anomalous events. To address this limitation, we propose
Image-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that
synthesizes event representations directly from RGB videos and fuses them with
image features through a principled, uncertainty-aware process. The system (i)
models heavy-tailed sensor noise with a Student`s-t likelihood, deriving
value-level inverse-variance weights via a Laplace approximation; (ii) applies
Kalman-style frame-wise updates to balance modalities over time; and (iii)
iteratively refines the fused latent state to erase residual cross-modal noise.
Without any dedicated event sensor or frame-level labels, IEF-VAD sets a new
state of the art across multiple real-world anomaly detection benchmarks. These
findings highlight the utility of synthetic event representations in
emphasizing motion cues that are often underrepresented in RGB frames, enabling
accurate and robust video understanding across diverse applications without
requiring dedicated event sensors. Code and models are available at
https://github.com/EavnJeong/IEF-VAD.