类自动车牌识别轨迹数据的伴随车辆组挖掘

摘要

自动车牌识别(ANPR)数据比私人全球定位系统(GPS)数据更易获得,且包含更有用的信息,但是相对成熟的针对GPS轨迹数据挖掘伴随车辆组方法并不适用于自动车牌识别数据,现有的少量自动车牌识别数据伴随车辆纽挖掘算法存在重视轨迹相似而忽视时间因素的缺陷,因此提出一种基于轨迹特征的聚类方法挖掘伴随车辆组.针对自动车牌识别数据中采样点固定而采样时间不定的特点,通过轨迹中共现的次数判定两个对象构成伴随模式.该共现定义引入豪斯多夫距离,综合考虑轨迹的地点、方向和时间特征,旨在挖掘数据中采样点不同但采样点距离近且轨迹相似的伴随车辆组,以此提高伴随车辆组挖掘效率.实验结果表明,所提方法较现有方法更能有效挖掘伴随车辆组,识别非伴随模式数据,效率提升了近两倍.%Automatic Number Plate Recognition (ANPR) data is easier to obtain than private Global Positioning System (GPS) data,and it contains more useful information,but the relatively mature GPS track data mining with vehicle group method did not apply to ANPR data,the existing accompanying vehicle group mining algorithm pays attention to the similarity of the trajectory and ignores the time factor when dealing with small amount of ANPR data.A clustering method based on trajectory feature to excavate the accompanying vehicle group was proposed.Aiming at the fact that the sampling points are fixed and the sampling time is uncertain in the ANPR data,whether two objects were accompanied was determined by the number of co-occurrence in the trajectory.The co-occurrence definition introduced the Hausdorff distance,taking into account the location,direction and time characteristics of the trajectory.The accompanying vehicle group with different but adjacent sampling points and similar trajectories was minned to improve the mining efficiency.The experimental results show that the proposed method is more effective than the existing method to excavate the vehicle group,and improves the efficiency by nearly two times when identifying the non-accompanying mode data.