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

• URL: http://arxiv.org/abs/2509.11605v2
• Date: Tue, 16 Sep 2025 01:19:35 GMT
• Title: DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection
• Authors: Seoik Jung, Taekyung Song, Joshua Jordan Daniel, JinYoung Lee, SungJun Lee,
• Abstract summary: Video Anomaly Detection (VAD) is critical for surveillance and public safety.<n>Existing benchmarks are limited to either frame-level or video-level tasks.<n>This work introduces a softmax-based frame allocation strategy that prioritizes anomaly-dense segments while maintaining full-video coverage.
• Score: 8.294763803639391
• License: http://creativecommons.org/licenses/by/4.0/
• 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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