基于特征的半固定多摄像头组件自动配置

Ann-Kristin Grosselfinger, David Münch, W. Hübner, Michael Arens
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引用次数: 1

摘要

自主操作的半固定式多相机组件是自组织多视图方法的核心模块。一方面,情况识别系统需要一个广角相机来提供整个场景的概览,另一方面,需要一个有趣的智能体的特写视图,例如一个主动的平移倾斜变焦(PTZ)相机,以进一步增加信息,例如识别这些智能体。为了配置这样一个系统,我们将全景相机的视场(FOV)设置为与PTZ相机的电机配置相对应。图像从均匀移动的PTZ相机捕获,直到主相机的整个视场被覆盖。在此过程中,生成PTZ相机的电机坐标和主相机中的图像坐标的查找表(LUT)。为了匹配每对图像,检测特征(SIFT, SURF, ORB, STAR, FAST, MSER, BRISK, FREAK),通过最近邻距离比(NNDR)选择并匹配。估计了将PTZ图像转换为主图像的单应性。有了这些信息,综合lut通过质心坐标计算,并存储主图像的每个像素。本文对不同特征的鲁棒性、准确性和运行时间进行了定量评价。
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Feature-based automatic configuration of semi-stationary multi-camera components
Autonomously operating semi-stationary multi-camera components are the core modules of ad-hoc multi-view methods. On the one hand a situation recognition system needs overview of an entire scene, as given by a wide-angle camera, and on the other hand a close-up view from e.g. an active pan-tilt-zoom (PTZ) camera of interesting agents is required to further increase the information to e.g. identify those agents. To configure such a system we set the field of view (FOV) of the overview-camera in correspondence to the motor configuration of a PTZ camera. Images are captured from a uniformly moving PTZ camera until the entire field of view of the master camera is covered. Along the way, a lookup table (LUT) of motor coordinates of the PTZ camera and image coordinates in the master camera is generated. To match each pair of images, features (SIFT, SURF, ORB, STAR, FAST, MSER, BRISK, FREAK) are detected, selected by nearest neighbor distance ratio (NNDR), and matched. A homography is estimated to transform the PTZ image to the master image. With that information comprehensive LUTs are calculated via barycentric coordinates and stored for every pixel of the master image. In this paper the robustness, accuracy, and runtime are quantitatively evaluated for different features.
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