RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics
Gur-Eyal Sela, Kumar Krishna Agrawal, Bharathan Balaji, Joseph Gonzalez, Ion Stoica

TL;DR
RedunCut is a system that optimizes live video analytics by intelligently sampling models and accurately predicting their performance, significantly reducing computational costs while maintaining accuracy across diverse video workloads.
Contribution
It introduces a measurement-driven planner and a lightweight performance model to improve sampling efficiency and accuracy prediction in dynamic model size selection for live video analytics.
Findings
RedunCut reduces compute cost by 14-62% at fixed accuracy.
It remains robust with limited historical data and data drift.
Effective across diverse video types and model families.
Abstract
Live video analytics (LVA) runs continuously across massive camera fleets, but inference cost with modern vision models remains high. To address this, dynamic model size selection (DMSS) is an attractive approach: it is content-aware but treats models as black boxes, and could potentially reduce cost by up to 10x without model retraining or modification. Without ground truth labels at runtime, we observe that DMSS methods use two stages per segment: (i) sampling a few models to calculate prediction statistics (e.g., confidences), then (ii) selection of the model size from those statistics. Prior systems fail to generalize to diverse workloads, particularly to mobile videos and lower accuracy targets. We identify that the failure modes stem from inefficient sampling whose cost exceeds its benefit, and inaccurate per-segment accuracy prediction. In this work, we present RedunCut, a new…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Anomaly Detection Techniques and Applications
