Topological and Statistical Behavior Classifiers for Tracking Applications
Topological and Statistical Behavior Classifiers for Tracking Applications
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DOI：
10.1109/taes.2016.160405
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发表时间：
2016-12-01
影响因子：
4.4
通讯作者：
Watkins, Adam
中科院分区：
文献类型：
--
作者：
Bendich, Paul;Chin, Sang Peter;Watkins, Adam
本文介绍了一种将目标行为与多假设跟踪（MHT）似然比相结合的方法。特别地，引入了基于行为的周期性轨迹评价。轨迹评估使用与基本机器学习技术相结合的基本拓扑数据分析，并且其调整传统的运动学数据关联可能性（即，跟踪分数），其使用用于特征辅助数据关联的已建立公式。所提出的方法进行了测试，并证明了合成车辆数据表示的城市交通场景的模拟城市流动性包。场景中的车辆表现出不同的驾驶行为。所提出的方法区分这些行为，并显示改进的数据关联决策相对于传统的，运动学MHT。
This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced. The track appraisal uses elementary topological data analysis coupled with basic machine-learning techniques, and it adjusts the traditional kinematic data association likelihood (i.e., track score) using an established formulation for feature-aided data association. The proposed method is tested and demonstrated on synthetic vehicular data representing an urban traffic scene generated by the Simulation of Urban Mobility package. The vehicles in the scene exhibit different driving behaviors. The proposed method distinguishes those behaviors and shows improved data association decisions relative to a conventional, kinematic MHT.