传感器网络中利用同调和霍奇分解的分散轨迹跟踪
Decentralized Trajectory Tracking Using Homology and Hodge Decomposition in Sensor Networks
摘要：
随着室内和室外环境的定位和跟踪系统的最新发展，我们考虑了感知，表示和分析我们希望在不久的将来收集的人类运动轨迹的问题。在本文中，我们建议使用拓扑表示，它记录了目标如何在底层环境中的自然障碍物周围移动。我们证明，拓扑信息可以充分描述许多应用程序和足够有效的存储，比较和分类这些自然的人类轨迹。我们用一个纯粹的分散算法对传感器网络进行预处理，使得某些边缘被赋予数值权重。然后，我们可以通过简单地对沿轨迹的边权重求和来执行轨迹分类。我们的方法支持实时分类的轨迹与最小的通信成本。我们测试我们的方法的有效性，展示如何分类随机生成的轨迹在多层次的艺术博物馆布局，以及如何区分现实世界中的出租车轨迹在一个大城市。
英文摘要：
With the recent development of localization and tracking systems for both indoor and outdoor settings, we consider the problem of sensing, representing and analyzing human movement trajectories that we expect to gather in the near future. In this paper, we propose to use the topological representation, which records how a target moves around the natural obstacles in the underlying environment. We demonstrate that the topological information can be sufficiently descriptive for many applications and efficient enough for storing, comparing and classifying these natural human trajectories. We pre-process the sensor network with a purely decentralized algorithm such that certain edges are given numerical weights. Then we can perform trajectory classification by simply summing up the edge weights along the trajectory. Our method supports real-time classification of trajectories with minimum communication cost. We test the effectiveness of our approach by showing how to classify randomly generated trajectories in a multi-level arts museum layout as well as how to distinguish real world taxi trajectories in a large city.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
