用于计算机视觉任务的有源摄像机网络的在线控制

A. Ilie, G. Welch
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引用次数: 9

摘要

大型摄像机网络越来越多地用于捕捉动态事件,用于监视和培训等任务。当使用活动摄像机捕捉分布在大范围内的事件时,人工控制变得不切实际和不可靠。这导致了在线相机控制自动化方法的发展。本文介绍了一种新的由随机性能度量和约束优化方法组成的自动摄像机控制方法。该度量量化了每个目标上多个点状态的不确定性。它使用状态空间方法与随机模型的目标动力学和相机测量。它可以考虑静态和动态遮挡,适应用于处理图像的算法的特定要求,并纳入可能影响其结果的其他因素。在与相机、预测目标轨迹和图像处理算法相关的约束下,优化探索了随时间变化的相机配置空间。该方法可以应用于传统的监视任务(例如,跟踪或面部识别),以及采用更复杂的计算机视觉方法的任务(例如,无标记运动捕捉或3D重建)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-line control of active camera networks for computer vision tasks
Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for on-line camera control. We introduce a new automated camera control approach that consists of a stochastic performance metric and a constrained optimization method. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of the target dynamics and camera measurements. It can account for static and dynamic occlusions, accommodate requirements specific to the algorithm used to process the images, and incorporate other factors that can affect its results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithm. The approach can be applied to conventional surveillance tasks (e.g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e.g., markerless motion capture or 3D reconstruction).
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