地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (2): 143-150.doi: 10.3724/SP.J.1047.2016.00143

• •    下一篇

移动对象多种运动参数在轨迹分类的应用

朱进1(), 江南2,3,**(), 胡斌2,3   

  1. 1. 苏州科技学院环境科学与工程学院,苏州 215009
    2. 南京师范大学 虚拟地理环境教育部重点实验室,南京 210023
    3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 收稿日期:2015-04-13 修回日期:2015-10-10 出版日期:2016-02-10 发布日期:2016-02-04
  • 通讯作者: 江南 E-mail:540896749@qq.com;njiang@njnu.edu.cn
  • 作者简介:

    作者简介:朱 进(1983-),男,江苏南京人,讲师,研究方向为轨迹数据挖掘。E-mail: 540896749@qq.com

  • 基金资助:
    环保公益性行业科研专项(201309037)

The Application of Multiple Movement Parameters in Trajectory Classification for Moving Objects

ZHU Jin1(), JIANG Nan2,3,*(), HU Bin2,3   

  1. 1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009,China
    2. Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2015-04-13 Revised:2015-10-10 Online:2016-02-10 Published:2016-02-04
  • Contact: JIANG Nan E-mail:540896749@qq.com;njiang@njnu.edu.cn

摘要:

轨迹分类是以训练轨迹的特征来预测未知轨迹的类标签,可进行可疑车辆识别、非法渔船检测和交通模式检测等重要应用。当前大多数轨迹分类方法只考虑速度和加速度这2个运动参数,且只利用简单的统计量(如均值、中值、最大值),不能充分挖掘轨迹的潜在特点,导致分类精度不高。针对该问题,本文在总结相关文献的基础上,提出一种基于移动对象运动特征的轨迹分类方法。针对速度、加速度、曲率、方向和转角这5个运动参数,利用偏度系数、峰度系数、变异系数和时间序列分析其中的自相关系数等,提取有区分力的全局运动特征;并从分割后的子轨迹中提取局部运动特征。对于方向和转角,引入方向统计学对其运动特征进行精确计算。实验表明本方法在船舶、野生动物和飓风数据集的分类精度达到了100%、80%和71.43%,实验验证了本方法构建的运动特征,在不同数据集下有效可行。

关键词: 时空轨迹, 轨迹分类, 运动特征, 交通模式, 支持向量机

Abstract:

The purpose of trajectory classification is to predict the class labels of unknown trajectories in terms of the trajectory characteristics. Trajectory classification has many real-world applications, for examples: suspicious vehicles identification, illegal fishing vessels detection, transportation mode detection, etc. Currently, most trajectory classification methods only take two movement parameters which are speed and acceleration into account, and only employ simple statistics such as the mean, median and maximum values, thus they can't fully explore the characteristics of trajectories, which leads to relatively low classification accuracy. In order to solve this problem, based on a thorough literature review on movement parameters and quantitative statistics, this paper proposes a trajectory classification method based on the movement characteristics of moving objects. For movement parameters of velocity, acceleration, sinuosity, direction and turning angle, this method employs statistics such as skewness, kurtosis, coefficient of variation and autocorrelation from time series analysis to construct discriminative global features. In addition, this method extracts local features from sub-trajectories after trajectory segmentation. For direction and turning angle, this method incorporates directional statistics to compute their features accurately. The experimental results of this method based on three real trajectory datasets including vessel, wild animal and hurricane datasets, indicate that the classification accuracies of this method are 100%, 80% and 71.43% respectively. The experiments verify the movement features constructed in this paper are discriminative and effective.

Key words: spatio-temporal trajectory, trajectory classification, movement characteristics, transportation mode, SVM