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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labeling
TL;DR
A Short-Window Sliding Learning framework is introduced for real-time violence detection in CCTV footage, achieving 95.25% accuracy on RWF-2000. This method enhances performance on longer videos, demonstrating strong generalization for intelligent surveillance systems.
✦ Why It Matters
Implement this framework to improve real-time violence detection in your surveillance systems today.
Key Takeaways
- Segment videos into 1-2 second clips for better analysis.
- Utilize LLMs for auto-caption labeling to enhance dataset quality.
- Aim for high accuracy metrics like 95.25% on RWF-2000.
- Consider real-time applicability for intelligent surveillance systems.
How It Works
The Short-Window Sliding Learning framework segments videos into short clips, allowing for detailed analysis of rapid events. By using LLM-based auto-caption labeling, it generates precise labels for each clip, ensuring that all frames contribute to the understanding of the event's context.
This method preserves temporal continuity, which is crucial for accurately detecting violence in fast-paced scenarios.