Elixir: A system to enhance data quality for multiple analytics on a video stream
Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas, Oliver, Po, Y. Charlie Hu, Srimat T. Chakradhar

TL;DR
Elixir is a system that uses multi-objective reinforcement learning to optimize camera settings, improving multiple video analytics tasks simultaneously despite environmental changes.
Contribution
It introduces a novel MORL-based approach with AU-specific quality estimators to enhance multi-analytics video streams in real-world IoT deployments.
Findings
Elixir improves detection rates for cars, faces, and persons significantly over baselines.
It detects substantially more license plates than other approaches.
Real-world experiments validate Elixir's effectiveness in diverse conditions.
Abstract
IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, healthcare, safety and security, transportation, manufacturing, etc. To amortize their high deployment effort and cost, it is desirable to perform multiple video analytics tasks, which we refer to as Analytical Units (AUs), off the video feed coming out of every camera. In this paper, we first show that in a multi-AU setting, changing the camera setting has disproportionate impact on different AUs performance. In particular, the optimal setting for one AU may severely degrade the performance for another AU, and further the impact on different AUs varies as the environmental condition changes. We then present Elixir, a system to enhance the video stream quality for multiple analytics on a video stream. Elixir leverages…
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Taxonomy
TopicsImage and Video Quality Assessment · Smart Parking Systems Research · Mobile Crowdsensing and Crowdsourcing
