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Recognition, pose and tracking of modelled polyhedral objects by multi-ocular vision

机译:通过多眼视觉识别,姿态和跟踪模型多面体对象

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We developped a fully automated algorithmic chain for the tracking of polyhedral objects with no manual intervention. It uses a multi-cameras calibrated system and the 3D model of the observed object. The initial phase of the tracking is done according to an automatic locationprocess using graph theoretical methods. The originality of the approach resides mainly in the fact that compound structures (triple junction and planar faces with four vertices) are used to construct the graphs describing scene and model. The association graph construction and the search of maximal cliques are greatly simplified in this way. The final solution is selected among the maximal cliques by a prediction-verification scheme. During the tracking process, it is noticeable that our model based approach does not use triangulation although the basis of the multi-ocular system is available. The knowledge of calibration parameters (extrinsic as well as intrinsic) of the cameras enables to express the various equations related to each images shot in one common reference system. The aim of this paper is to prove that model based methods are not bound to monocular schemes but can be used in various multi-ocular situations in which they can improve the overall robustness.
机译:我们开发了一个全自动算法链,用于跟踪多面体对象,没有手动干预。它使用了多摄像机校准系统和观察对象的3D模型。跟踪的初始阶段根据使用曲线图理论方法的自动位置处理完成。该方法的原创性主要在于,用于构建描述场景和模型的图形来构建描述场景和模型的图形的组合结构(三界和平面面)。以这种方式大大简化了关联图构造和最大批判的搜索。通过预测验证方案在最大批变中选择最终解决方案。在跟踪过程中,虽然多眼系统的基础可用,但我们基于模型的方法不使用三角测量是值得注意的。相机的校准参数(外部和内在)的知识使得能够以一个公共参考系统中拍摄的每个图像相关的各种方程式。本文的目的是证明基于模型的方法不受单眼方案的束缚,但可以用于各种多眼间,它们可以改善整体鲁棒性。

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