基于几何的鱼眼立体人物再识别

Joshua Bone, Mertcan Cokbas, M. Tezcan, J. Konrad, P. Ishwar
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引用次数: 3

摘要

迄今为止,利用直线摄像机对人的再识别进行了深入的研究。然而,这个话题很少受到鱼眼相机的关注,并且少数开发的方法是基于外观的。我们提出了一种基于几何的方法来重新识别具有重叠视场的头顶鱼眼相机。主要思想是,在两个摄像头视图中可见的人在一个摄像头的视图中是唯一的,给定他们在另一个摄像头视图中的高度和位置。我们利用全向相机的统一球面模型,在这些位置之间建立了高度相关的数学关系。我们还提出了一种新的鱼眼相机标定方法和一种新的自动标定数据采集方法。最后,在我们收集的鱼眼相机数据集上,我们提出了四种利用几何约束的重新识别算法,并证明了它们出色的准确性,远远超过了最先进的基于外观的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Geometry-Based Person Re-Identification in Fisheye Stereo
Person re-identification using rectilinear cameras has been thoroughly researched to date. However, the topic has received little attention for fisheye cameras and the few developed methods are appearance-based. We propose a geometry-based approach to re-identification for overhead fisheye cameras with overlapping fields of view. The main idea is that a person visible in two camera views is uniquely located in the view of one camera given their height and location in the other camera’s view. We develop a height-dependent mathematical relationship between these locations using the unified spherical model for omnidirectional cameras. We also propose a new fisheye-camera calibration method and a novel automated approach to calibration-data collection. Finally, we propose four re-identification algorithms that leverage geometric constraints and demonstrate their excellent accuracy, which vastly exceeds that of a state-of-the-art appearance-based method, on a fisheye-camera dataset we collected.
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