Enhancing Autofocus Performance through Predictive Motion-Targeting and Self-Attention in a Deep Reinforcement Learning Framework

In focusing tasks on moving targets, traditional methods that rely on maximizing contrast struggle to capture moving objects due to insufficient focusing speed. Deep learning-based methods have attempted to directly predict the optimal focal length for the target; however, due to low prediction accuracy, they often lead to out-of-focus situations when capturing moving objects. In recent years, some approaches have utilized reinforcement learning to automatically explore focal length adjustment patterns, thus achieving better results than traditional methods. However, these approaches have not considered the motion characteristics of the targets, leading to a need for further improvement in focusing performance. To overcome these limitations, we introduce a motion-based feature and deep reinforcement learning-driven autofocus algorithm named MF-DRLAF (Motion Features based Deep Reinforcement Learning Autofocus Model) for moving targets. This novel method tracks the object, predicts its motion state through feature extraction, and uses deep reinforcement learning to dynamically adjust the focus. We utilize a self-attention mechanism to adaptively learn various motion patterns and employ a feature pool structure to enhance processing efficiency. Experiments and real-world testing on a Google Pixel3 demonstrate that our approach significantly enhances autofocus performance on moving objects, highlighting its potential for broader imaging applications. This approach offers a promising direction for future development in autofocus technology.