Visual surveillance systems play a vital role in smart manufacturing scenes. In this paper, a cooperative camera surveillance method is proposed based on the coarse-fine coupling boresight adjustment principle. A master-slave camera cooperative monitoring model is established by combining a high-resolution camera with a large-field camera, the former of which is embedded with a Risley prism pair to perform high-precision boresight pointing for monitoring a local target, while the latter can observe a wide scene and guide the former to catch a region of interest. An effective boresight adjustment strategy is put forward with a method of combining fast convergence and iterative refinement. A calibration method of the master-slave camera is proposed with the combination of binocular vision and equivalent motion plane. The error distribution, adjustment efficiency and tracking accuracy of the dual field monitoring are analyzed in detail to reveal the mapping mechanism between imaging feedback and boresight adjustment. In the surveillance experiment, the root-mean-square tracking error of the fine tracking camera can achieve better than 7.04 pixels, while the adjustment steps only need about 10 times, which validates the feasibility and effectiveness of the proposed method in industrial operation surveillance.

视觉监控系统在智能制造场景中起着至关重要的作用。本文提出了一种基于粗细耦合视轴调整原理的协同摄像机监视方法。通过将高分辨率摄像机与大视野摄像机相结合，建立了主从摄像机协作监视模型，该大视野摄像机中嵌入了Risley棱镜对以执行高精度的视轴指向，以监视本地目标。后者可以观察广阔的场景并引导前者捕捉感兴趣的区域。提出了一种有效的视轴调整策略，结合了快速收敛和迭代精细化的方法。提出了一种将双目视觉和等效运动平面相结合的主从摄像机的标定方法。错误分布 详细分析了双场监测的调整效率和跟踪精度，揭示了成像反馈与视轴调整之间的映射机制。在监控实验中，精细跟踪相机的均方根跟踪误差可以达到7.04像素以上，而调整步骤仅需10次左右，验证了该方法在工业运行监控中的可行性和有效性。