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A structure-based approach for optimizing distributed reconstruction in Motion Capture systems

机译：一种基于结构的方法，用于优化运动捕捉系统中分布式重建的方法

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The diffusion of visual sensor networks, and in particular of smart camera networks, is motivating an increasing interest on the research of distributed solutions for several vision problems. Specifically, in this paper we propose a distributed solution to the problem of reconstructing target positions in large Motion Capture (MoCap) systems. Real time reconstruction by means of centralized procedures is practically unfeasible for very large systems, while the use of distributed computation allows to significantly reduce the computational time required for reconstruction, thus allowing the development of real time solutions. Then the proposed distributed reconstruction procedure is optimized by exploiting information about the structure of the system: the visibility matrix states which objects in the scene are somehow measurable by a sensor (sensor-object matrix). Often, the typical localization of data from real application scenarios induces an underlying structure on the visibility matrix, that can be exploited to improve the performance of the system in understanding the surrounding environment. Unfortunately, usually these data are not properly organized in the visibility matrix: for instance, listing the sensors in a pseudo-random order can hide the underlying structure of the matrix. This paper considers the problem of recovering such underlying structure directly from the visibility matrix and designs an algorithm to perform this task. Our simulations show that the distributed reconstruction algorithm optimized by means of the estimation of the structure of the visibility matrix achieves an important computational time reduction with respect to the standard (centralized) reconstruction algorithm.
机译：视觉传感器网络和特别是智能摄像机网络的扩散是对几个视觉问题的分布式解决方案的研究的兴趣越来越兴趣。具体地，在本文中，我们向大型运动捕获（MoCAP）系统中重建目标位置的问题提出了分布式解决方案。通过集中过程的实时重建实际上对于非常大的系统来说实际上是不可行的，而分布式计算的使用允许显着减少重建所需的计算时间，从而允许开发实时解决方案。然后通过利用有关系统结构的信息来优化所提出的分布式重建程序：可见性矩阵状态在某些对象中的某些物体由传感器（传感器对象矩阵）可测量。通常，来自实际应用场景的数据的典型本地化在可见性矩阵上引起底层结构，可以利用以提高系统在理解周围环境方面的性能。不幸的是，通常这些数据在可见性矩阵中未被正确组织：例如，以伪随机顺序列出传感器可以隐藏矩阵的基础结构。本文考虑了直接从可见性矩阵恢复此类底层结构的问题，并设计一种执行此任务的算法。我们的模拟表明，通过估计可见性矩阵结构的估计优化的分布式重建算法实现了关于标准（集中式）重建算法的重要计算时间。

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