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Uncalibrated Framework for On-line Camera Cooperation to Acquire Human Head Imagery in Wide Areas

机译:在线相机合作的未校准框架获取广泛领域的人头图像

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This paper considers the problem of estimating on-line the time-variant transformation relating a person's feet position in the image of a first, fixed camera, to his head position in the image of a second, pan-tilt-zoom camera. The transformation allows to acquire high-resolution images by steering the PTZ camera at targets detected in a fixed camera view. Assuming a planar scene and modeling humans as vertical segments, we present the development of an uncalibrated framework which does not require any 3D known location to be specified, and it allows to take into account both zooming camera and target uncertainties. Results show good performances in slave camera target head localization, degrading when the high zoom factor causes a lack of feature points in the slave camera.
机译:本文考虑估计在线时变形变换在第一,固定摄像机的图像中与第二泛倾斜放大摄像机图像中的头部位置相关的时变形变换的问题。该转换允许通过在固定相机视图中检测到的目标处转向PTZ相机来获取高分辨率图像。假设平面场景和将人类建模为垂直段,我们介绍了不需要指定任何3D已知位置的未校准框架的开发,并且允许考虑缩放相机和目标不确定性。结果显示奴隶相机目标头定位的良好性能,当高缩放因子导致从属摄像机中缺少特征点时,降低。

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