Summary of Learning to Control Camera Exposure Via Reinforcement Learning, by Kyunghyun Lee et al.
Learning to Control Camera Exposure via Reinforcement Learning
by Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new camera exposure control framework that uses deep reinforcement learning to rapidly adjust camera exposure while performing real-time processing. The framework consists of four contributions: a simplified training ground, flickering and image attribute-aware reward design, a static-to-dynamic lighting curriculum, and domain randomization techniques. The proposed method achieves a desired exposure level within five steps with real-time processing (1 ms) and produces well-exposed images that show superiority in computer vision tasks such as feature extraction and object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure the camera on a computer takes good pictures even when the lighting changes suddenly. Right now, this process can take a long time and doesn’t work well in changing lighting conditions. The researchers created a new way to control camera exposure that uses machine learning and can adjust quickly to different lighting situations. This new method is fast (1 millisecond) and produces better images than before. |
Keywords
» Artificial intelligence » Feature extraction » Machine learning » Object detection » Reinforcement learning
