标题： 用于跟踪应用的拓扑和统计行为分类器 显示英文标题

标题： Topological and Statistical Behavior Classifiers for Tracking Applications

摘要： 我们提出了首个用于目标跟踪的统一理论，该理论结合了多假设跟踪（MHT）、拓扑数据分析（TDA）和机器学习方法。我们的创新点包括：1）使用鲁棒的拓扑特征来编码行为信息；2）针对这些拓扑特征上的分布拟合统计模型；3）采用Wigren和Bar-Shalom等人的目标类型分类方法，在跟踪过程中利用由此产生的拓扑特征似然性。为了验证我们方法的有效性，我们在由城市移动性模拟包生成的合成车辆数据上测试了我们的方法。 显示英文摘要

摘要： We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning. Our string of innovations are 1) robust topological features are used to encode behavioral information, 2) statistical models are fitted to distributions over these topological features, and 3) the target type classification methods of Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods for topological features inside of the tracking procedure. To demonstrate the efficacy of our approach, we test our procedure on synthetic vehicular data generated by the Simulation of Urban Mobility package.