Research Paper#Computer Vision, Video Analytics, AI Optimization🔬 ResearchAnalyzed: Jan 3, 2026 09:31
RedunCut: Cost-Effective Live Video Analytics
Published:Dec 30, 2025 18:01
•1 min read
•ArXiv
Analysis
This paper addresses the high computational cost of live video analytics (LVA) by introducing RedunCut, a system that dynamically selects model sizes to reduce compute cost. The key innovation lies in a measurement-driven planner for efficient sampling and a data-driven performance model for accurate prediction, leading to significant cost reduction while maintaining accuracy across diverse video types and tasks. The paper's contribution is particularly relevant given the increasing reliance on LVA and the need for efficient resource utilization.
Key Takeaways
- •RedunCut is a Dynamic Model Size Selection (DMSS) system for live video analytics.
- •It uses a measurement-driven planner for efficient sampling.
- •It employs a data-driven performance model to improve accuracy prediction.
- •RedunCut achieves significant compute cost reduction (14-62%) while maintaining accuracy.
- •The system is robust to limited historical data and data drift.
Reference
“RedunCut reduces compute cost by 14-62% at fixed accuracy and remains robust to limited historical data and to drift.”