一种卡口车辆轨迹相似度算法的研究和实现  

Research and implementation of a vehicle trajectory similarity algorithm used for security access monitoring

作　　者：樊志英[1] 

机构地区：[1]公安部第一研究所,北京100048

出　　处：《现代电子技术》2016年第23期133-135,140,共4页Modern Electronics Technique

基　　金：十二五国家科技支撑计划"基于视频及动态信息的智能研判技术研究及应用示范":基于重点目标自动跟踪采集技术的智能视频监控系统研发(2013BAK02B02)

摘　　要：依据车辆轨迹相似度在时间和空间维度上的约束,引入LCSS算法,遵循最长公共子序列的原理,抽象出轨迹中的卡口号序列,提出一种两条车辆轨迹相似度的计算方法,并结合Spark并行计算、Hive数据仓库存储等相关技术,搭建数据分析平台,实现该算法。实验表明,该算法满足实际车辆轨迹在时间和空间上的相似性,数据分析计算在性能上可以满足前台业务的检索。该算法和轨迹相似度分析业务,可作为治安卡口应用系统中关联车辆分析、团伙作案车辆分析等功能的后台支撑业务。According to the constraints of time and space dimensions of the vehicle trajectory similarity, the LCSS （longest common subsequence） algorithm is proposed. According to the principle of longest common subsequence, the access monitoring sequences in the trajectory are abstracted. A calculation method of two vehicle trajectories similarity is proposed. The Spark pa- rallel calculation, Hive data warehouse storage and other correlation technologies are combined to establish the data analysis platform, and implement the algorithm. The experimental results show that the algorithm can satisfy the time and space similari- ty of the practical vehicle trajectory, and the data analysis and calculation can meet the search performance of foreground busi- ness. The algorithm and trajectory similarity analysis business can be used as the background support service of the vehicle rele- vance analysis and gang crime vehicle analysis in the security access monitoring application system.

关 键 词：轨迹相似度 LCSS算法 SPARK Hive 

分 类 号：TN911-34[电子电信—通信与信息系统] TP311.5[电子电信—信息与通信工程]