吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1696-1702.doi: 10.13229/j.cnki.jdxbgxb201706004

• 论文 • 上一篇    下一篇

高速公路出入口运动车辆轨迹分层聚类算法

孙宗元, 方守恩   

  1. 同济大学 道路与交通工程教育部重点试验室,上海 201804
  • 收稿日期:2016-07-13 出版日期:2017-11-20 发布日期:2017-11-20
  • 通讯作者: 方守恩(1961-),男,教授,博士生导师.研究方向:道路交通安全,道路规划与设计.E-mail:fangshouen@tongji.edu.cn
  • 作者简介:孙宗元(1984-),男,博士研究生.研究方向:道路交通安全,道路规划与设计.E-mail:15843071080@163.com
  • 基金资助:
    “863”国家高技术研究发展计划项目(2013AA12A206)

Hierarchical clustering algorithm of moving vehicle trajectories in entrances and exits freeway

SUN Zong-yuan, FANG Shou-en   

  1. Key Laboratory of Road and Traffic Engineering of Ministry of Education,Tongji University,Shanghai 201804,China
  • Received:2016-07-13 Online:2017-11-20 Published:2017-11-20

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摘要： 为了提高对高速公路出入口车辆运动行为的理解和分析水平,根据出入口车辆运动轨迹的时空特征,提出了一种运动轨迹层次聚类算法。结合出入口轨迹方向一致、长短不一的特点,提出采用改进Hausdorff距离来衡量轨迹间的相似性。建立了改进模糊C均值轨迹分层聚类算法,首先根据轨迹的空间几何位置进行路径聚类,然后根据车辆的速度信息对已有路径聚类进一步聚类获得具有时空区分度的最终结果。真实高速公路出入口的试验结果表明:本文提出的轨迹聚类算法对于场景固定运动行为模式不仅具有较强的适用性,而且能够保障聚类结果的准确性和可靠性。

关键词: 交通运输系统工程, 高速公路出入口, 轨迹分析, 改进Hausdorff距离, 聚类算法

Abstract: In order to improve the understanding and analysis of motion patterns of vehicles, a hierarchical trajectory clustering algorithm is developed according to spatial and temporal characteristics of the vehicle trajectories in the entrances and exits of freeway. In view of the vehicle trajectories are different in length, but in the same direction, the improved Hausdorff distance was proposed and applied to measure the similarity of trajectories. The improved fuzzy C-means hierarchical clustering algorithm of trajectories was further established, in which trajectories were first clustered into different paths according to the spatial geometric position of the trajectories, and then trajectories belonging to the same path were further clustered according to the vehicle speed to obtain the final results with spatial and temporal degree. Experiments in the entrances and exits of freeway were carried out. The results confirm that the proposed trajectory clustering algorithm not only has strong adaptability to the inherent motion pattern of the scene, but also ensures the accuracy and reliability of the clustering results.

Key words: engineering of communication and transportation system, freeway entrances and exits, trajectories analysis, improved Hausdorff distance, clustering algorithm

中图分类号: 

  • U491
[1] Valera M, Velastin S A. Intelligent distributed surveillance systems: a review[J]. IEE Proceedings-Vision, Image and Signal Processing,2005,152(2):192-204.
[2] Morris B T, Trivedi M M. A survey of vision-based trajectory learning and analysis for surveillance[J]. IEEE Transactions on Circuits & Systems for Video Technology,2008,18(8):1114-1127.
[3] 潘奇明,程咏梅. 基于隐马尔可夫模型的运动目标轨迹识别[J]. 计算机应用研究,2008,25(7):1988-1991.
Pan Qi-ming, Cheng Yong-mei. Trajectory recognition of moving objects based on hidden Markov model[J]. Application Research of Computers,2008,25(7):1988-1991.
[4] 郝久月,李超,高磊,等. 智能监控场景中运动目标轨迹聚类算法[J].北京航空航天大学学报,2009,35(9):1083-1087.
Hao Jiu-yue,Li Chao,Gao Lei, et al. Moving object trajectory clustering method in intelligent surveillance video[J]. Journal of Beijing University of Aeronautics and Astronautics,2009,35(9):1083-1087.
[5] Hu W,Xiao X,Fu Z, et al. A system for learning statistical motion patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2006,28(9):1450-1464.
[6] Sumpter N, Bulpitt A. Learning spatio-temporal patterns for predicting object behaviour[J]. Image & Vision Computing,1998,18(9):697-704.
[7] 袁和金,吴静芳,贾建军. 一种基于HMM聚类的视频目标轨迹分析方法[J]. 华北电力大学学报:自然科学版,2010,37(6):90-94.
Yuan He-jin,Wu Jing-fang,Jia Jian-jun. A method of video target's trajectory analysis based on HMM clustering[J].Journal of North China Electric Power University(Natural Science),2010,37(6):90-94.
[8] Saunier N, Sayed T. Automated road safety analysis using video data[J]. Transportation Research Record Journal of the Transportation Research Board, 2007,2019(2019):57-64.
[9] Stauffer C, Grimson W E L. Learning patterns of activity using real-time tracking[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2000,22(8):747-757.
[10] Xu R, Nd W D. Survey of clustering algorithms[J]. IEEE Transactions on Neural Networks,2005,16(3):645-678.
[11] Zhang Z, Huang K, Tan T. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes[C]//International Conference on Pattern Recognition, Hong Kong, China,2006:1135-1138.
[12] Khalid S, Naftel A. Evaluation of matching metrics for trajectory-based indexing and retrieval of video clips[C]//Proceedings of the Seventh IEEE Workshops on Application of Computer Vision,Breckenridge,USA,2005:242-249.
[13] Atev S, Masoud O, Papanikolopoulos N. Learning traffic patterns at intersections by spectral clustering of motion trajectories[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing,China,2006:4851-4856.
[14] 王俊骅,张方方,张兰芳. 基于OpenCV和Halcon的交通冲突视频自动检测及数据处理[J]. 同济大学学报:自然科学版,2010,38(2):238-244.
Wang Jun-hua, Zhang Fang-fang, Zhang Lan-fang. Halcon and OpenCV-based traffic automatic conflicting detecting method and data transaction[J]. Journal of Tongji University(Natural Science),2010,38(2):238-244.
[15] 李明之,马志强,单勇,等. 交通监控中运动目标轨迹的距离计算和聚类[J].计算机工程与设计,2012,33(6):2417-2422.
Li Ming-zhi, Ma Zhi-qiang, Shan Yong, et al. Distance calculating and clustering of moving objects'trajectories in traffic surveillance video[J]. Computer Engineering and Design,2012,33(6):2417-2422.
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