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About this paper
Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee
DRL-AE, a deep reinforcement learning framework, achieves rapid and robust automatic camera exposure control. It converges within 3-5 frames and processes in 1ms on a CPU, significantly outperforming traditional methods and improving performance for downstream computer vision tasks in real-world driving environments.
Key insights
- Deep Reinforcement Learning (DRL) can effectively learn and manage complex, continuous camera control policies, providing stable and rapid adjustments that surpass the performance limitations of traditional optimization and supervised methods.
- A lightweight state representation (vectorized intensity history) combined with relative action control is sufficient and crucial for enabling rapid convergence and preventing flickering in dynamic exposure control.
- Training in a controlled real-world environment (darkroom) augmented with spatial domain randomization successfully bridges the 'darkroom-to-wild' gap, allowing DRL-AE to generalize effectively to diverse, unseen real-world scenarios with zero-shot performance.
Summary by alphaXiv.
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