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An effective method for extrinsic calibration of network cameras with non-overlapping field of views

机译:具有非重叠视野的网络摄像机外部校准的有效方法

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This paper proposes an effective method for determining the extrinsic parameters (the locations and orientations of the cameras in a relative coordinate system), named as extrinsic calibration, for sparse network cameras, which are commonly utilized for covering much larger area. The proposed method formulates the problem as a Maximum A Posteriori (MAP) estimation, and calculates the parameters with Newton-Raphson algorithm based on the cameras' observations of an object freely moving in the cameras' field of views. One of the method's advantages is that it significantly decreases the computation time, since a step length is properly chosen and put in Newton-Raphson algorithm. The results herein demonstrate the superior performance of the proposed method over the state-of-the-art approaches based on the classical evaluation metric in simulation experiments.
机译:本文提出了一种有效的方法来确定稀疏网络摄像机的外部参数(摄像机在相对坐标系中的位置和方向),称为外部校正,该方法通常用于覆盖更大的区域。所提出的方法将问题表示为最大后验(MAP)估计,并基于摄像机对在摄像机视场中自由移动的对象的观察,使用牛顿-拉夫森算法计算参数。该方法的优点之一是,由于正确选择了步长并将其放入Newton-Raphson算法中,因此大大减少了计算时间。本文的结果证明了所提出的方法在模拟实验中优于基于经典评估指标的最新技术。

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