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LLM-powered auto-labeling enhances real-time violence detection in CCTV footage

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Short-Window Sliding Learning for real-time violence detection using closed-circuit television footage. This method divides videos into short clips and employs Large Language Models (LLMs) for auto-labeling, creating detailed datasets. The approach preserves temporal continuity within each clip, allowing for accurate recognition of rapid violent actions. Experiments show high accuracy on benchmark datasets like RWF-2000 and improved performance on longer videos from UCF-Crime, indicating its effectiveness for intelligent surveillance. AI

IMPACT This research could lead to more effective and efficient AI-powered surveillance systems for public safety.

RANK_REASON Academic paper detailing a new method for violence detection using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-powered auto-labeling enhances real-time violence detection in CCTV footage

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Academic paper detailing a new method for violence detection using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Seoik Jung, Taekyung Song, Yangro Lee, Sungjun Lee ·

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

    arXiv:2511.10866v2 Announce Type: replace-cross 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 …

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