Reinforcement-learning based system for camera parameter tuning to improve analytics
US12347143B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 26, 2022 |
| Grant date | Jul 1, 2025 |
| Priority date | — |
| Expiry date | Jan 21, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.