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

• URL: http://arxiv.org/abs/2511.10866v1
• Date: Fri, 14 Nov 2025 00:29:31 GMT
• Title: Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling
• Abstract summary: This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages.<n>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.
• Score: 0.6999740786886536
• License: http://creativecommons.org/licenses/by/4.0/
• 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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