Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling
cs.CV, cs.AI
Submitted: 2025-11-14
Updated: 2026-09-03
Comments: 5 pages, 2 figures. Accepted paper for the IEIE (Institute of Electronics and Information Engineers) Fall Conference 2025. Presentation on Nov 27, 2025
License: http://creativecommons.org/licenses/by/4.0/
The gist: This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages.
Terminology
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.
Sources
- DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
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