Automatic visual enhancement of PTZ camera based on reinforcement learning

Video surveillance systems have become indispensable for enhancing border security and effectively addressing threats such as illegal border crossings. Object detection plays a crucial role in real-time monitoring and event response in these systems. However, challenges arise in border scenarios, where targets exhibit small sizes, low resolutions, and limited extractable features, resulting in lower detection confidence. To overcome these limitations, we propose an advanced pan–tilt–zoom camera control method that does not require intrinsic camera parameters. The objective of this study is to accomplish visual enhancement tasks for low-confidence targets. The proposed method employs deep reinforcement learning techniques, integrating both discrete and continuous action spaces to enhance the generalization capability of agent decisions across diverse scenarios, thereby achieving optimal target monitoring. In addition, the introduction of the cutout feature fusion filter enables the agent to focus equally on each target in multitarget scenarios. In our experiments, we compared the proposed method with other approaches. The results demonstrate the superiority of the proposed method in various scenarios and object detection algorithms.