Learning to Control Camera Exposure via Reinforcement Learning

Abstract

Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of com-puter vision applications. Poorly adjusted camera expo-sure 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. The proposed framework consists of four contributions: 1) a simplified training ground to simulate real-world's diverse and dynamic lighting changes, 2) flickering and image attribute-aware reward design, along with lightweight state design for real-time processing, 3) a static-to-dynamic lighting curriculum to gradually improve the agent's exposure-adjusting capability, and 4) domain randomization techniques to alleviate the limitation of the training ground and achieve seamless generalization in the wild. As a result, our proposed method rapidly reaches a desired exposure level within five steps with real-time processing (1ms). Also, the acquired images are well-exposed and show superiority in various computer vision tasks, such as feature extraction and object detection.