SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere

TL;DR

SphereVAD introduces a training-free, geometric inference-based framework for video anomaly detection that leverages pre-trained multimodal models and spherical geometry to identify anomalies without task-specific training.

Contribution

It proposes a novel zero-shot VAD method using geodesic inference on the hypersphere, eliminating the need for large-scale annotations or training.

Findings

Achieves state-of-the-art results among training-free methods on major benchmarks.

Remains competitive with fully supervised approaches.

Requires only minimal synthetic images for calibration.

Abstract

Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large-scale annotations or task-specific training procedures, severely limiting their rapid deployment to novel scenes. We observe that intermediate-layer features of pre-trained multimodal large language models (MLLMs) already encode rich anomaly semantics, yet existing approaches rely on the language output pathway and fail to exploit the geometric discriminability latent in these representations. Based on this finding, we propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises-Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere, unleashing latent discriminability through principled geometric reasoning rather than learning new representations. Specifically, SphereVAD first applies Frechet mean centering to unfold feature distributions and eliminate domain biases, then employs Holistic Scene Attention (HSA) to reinforce feature consistency using cross-video priors, and finally performs vMF-guided Spherical Geodesic Pulling (SGP) to align ambiguous segments with directional prototypes on the spherical manifold. This training-free pipeline requires only minimal synthetic images for calibration. SphereVAD establishes new state-of-the-art results among training-free approaches on three major benchmarks and remains competitive with fully supervised baselines. Code will be available upon acceptance.