Learning to Control Camera Exposure via Reinforcement Learning

arXiv:2404.01636v1 Announce Type: cross
Abstract: Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications. Poorly adjusted camera exposure often leads to critical failure and performance degradation. Traditional camera exposure control methods require multiple convergence steps and time-consuming processes, making them unsuitable for dynamic lighting conditions. In this paper, we propose a new camera exposure control framework that rapidly controls camera exposure while performing real-time processing by exploiting deep reinforcement learning. …

abstract adjusting applications arxiv computer computer vision control convergence cs.ai cs.cv cs.lg cs.ro cs.sy eess.sy failure leads lighting making multiple performance processes reinforcement reinforcement learning them type via vision