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Calibrating a Wide-Area Camera Network with Non-Overlapping Views Using Mobile Devices

机译:使用移动设备校准不重叠视图的广域相机网络

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In a wide-area camera network, cameras are often placed such that their views do not overlap. Collaborative tasks such as tracking and activity analysis still require discovering the network topology including the extrinsic calibration of the cameras. This work addresses the problem of calibrating a fixed camera in a wide-area camera network in a global coordinate system so that the results can be shared across calibrations. We achieve this by using commonly available mobile devices such as smartphones. At least one mobile device takes images that overlap with a fixed camera's view and records the GPS position and 3D orientation of the device when an image is captured. These sensor measurements (including the image, GPS position, and device orientation) are fused in order to calibrate the fixed camera. This article derives a novel maximum likelihood estimation formulation for finding the most probable location and orientation of a fixed camera. This formulation is solved in a distributed manner using a consensus algorithm. We evaluate the efficacy of the proposed methodology with several simulated and real-world datasets.
机译:在广域摄像机网络中,摄像机的放置位置通常不会重叠。诸如跟踪和活动分析之类的协作任务仍然需要发现网络拓扑,包括摄像机的外部校准。这项工作解决了在全局坐标系中的广域相机网络中校准固定相机的问题,以便可以在校准之间共享结果。我们通过使用常见的移动设备(例如智能手机)来实现这一目标。至少一个移动设备拍摄与固定摄像机视图重叠的图像,并在捕获图像时记录该设备的GPS位置和3D方向。将这些传感器测量值(包括图像,GPS位置和设备方向)融合在一起,以校准固定摄像机。本文导出了一种新颖的最大似然估计公式,用于查找固定摄像机的最可能的位置和方向。使用共识算法以分布式方式解决该公式。我们用几个模拟的和真实的数据集评估了所提出方法的有效性。

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