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.
@article{arxiv.2511.10866,
title = {Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling},
author = {Seoik Jung and Taekyung Song and Yangro Lee and Sungjun Lee},
journal= {arXiv preprint arXiv:2511.10866},
year = {2025}
}
Comments
5 pages, 2 figures. Accepted paper for the IEIE (Institute of Electronics and Information Engineers) Fall Conference 2025. Presentation on Nov 27, 2025