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arXiv · 2509.11605

DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection

Seoik Jung·Taekyung Song·Joshua Jordan Daniel·JinYoung Lee·SungJun Lee
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Abstract

Video Anomaly Detection (VAD) is critical for surveillance and public safety. However, existing benchmarks are limited to either frame-level or video-level tasks, restricting a holistic view of model generalization. This work first introduces a softmax-based frame allocation strategy that prioritizes anomaly-dense segments while maintaining full-video coverage, enabling balanced sampling across temporal scales. Building on this process, we construct two complementary benchmarks. The image-based benchmark evaluates frame-level reasoning with representative frames, while the video-based benchmark extends to temporally localized segments and incorporates an abnormality scoring task. Experiments on UCF-Crime demonstrate improvements at both the frame and video levels, and ablation studies confirm clear advantages of anomaly-focused sampling over uniform and random baselines.

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Seoik Jung, Taekyung Song, Joshua Jordan Daniel, JinYoung Lee, SungJun Lee. 2025-09-15. DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection. https://arxiv.org/abs/2509.11605

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