论文标题
Diot：检测轨迹的隐式障碍
DIOT: Detecting Implicit Obstacles from Trajectories
论文作者
论文摘要
在本文中，我们研究了轨迹数据中障碍物检测的新数据挖掘问题。在直觉上，给定两种轨迹，即参考和查询轨迹，障碍物是一个区域，因此大多数查询轨迹都需要绕过该区域，而参考轨迹可以照常进行。我们基于新的归一化动态时间翘曲（NDTW）距离以及针对子射击量的密度函数引入了基于密度的定义，以估计密度变化。通过此定义，我们介绍了一个新颖的框架\ textsf {diot}，该{diot}利用深度优先的搜索方法来检测隐式障碍。我们对两个现实生活数据集进行了广泛的实验。实验结果表明，\ textsf {diot}可以捕获障碍的性质，但有效地检测了隐式障碍。代码可在\ url {https://github.com/1flei/obstacle}中获得。
In this paper, we study a new data mining problem of obstacle detection from trajectory data. Intuitively, given two kinds of trajectories, i.e., reference and query trajectories, the obstacle is a region such that most query trajectories need to bypass this region, whereas the reference trajectories can go through as usual. We introduce a density-based definition for the obstacle based on a new normalized Dynamic Time Warping (nDTW) distance and the density functions tailored for the sub-trajectories to estimate the density variations. With this definition, we introduce a novel framework \textsf{DIOT} that utilizes the depth-first search method to detect implicit obstacles. We conduct extensive experiments over two real-life data sets. The experimental results show that \textsf{DIOT} can capture the nature of obstacles yet detect the implicit obstacles efficiently and effectively. Code is available at \url{https://github.com/1flei/obstacle}.
