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arXiv · 2511.10866

Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling

Seoik Jung·Taekyung Song·Yangro Lee·Sungjun Lee
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Abstract

This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages. Unlike conventional long-video training approaches, the proposed method divides videos into 1-2 second clips and applies Large Language Model (LLM)-based auto-caption labeling to construct fine-grained datasets. Each short clip fully utilizes all frames to preserve temporal continuity, enabling precise recognition of rapid violent events. Experiments demonstrate that the proposed method achieves 95.25\% accuracy on RWF-2000 and significantly improves performance on long videos (UCF-Crime: 83.25\%), confirming its strong generalization and real-time applicability in intelligent surveillance systems.

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Seoik Jung, Taekyung Song, Yangro Lee, Sungjun Lee. 2025-11-14. Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling. https://arxiv.org/abs/2511.10866

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