摘要
车牌识别数据是一种具有数据量大、时空相关、位置可测等特征的车辆监测数据,基于此类数据的相似轨迹查询面临着诸多问题。该文给出一种基于"点伴随关系"的车辆相似轨迹定义,提出了一种多级任务并行的相似轨迹查询方法,并给出了基于MapReduce迭代计算模型的方法实现,可支持在海量车牌识别数据集中利用分布计算环境高效地完成相似轨迹查询。基于近千万条真实车牌识别数据的实验表明,相对于传统方法,该方法在保证相似轨迹查询结果准确的前提下具有更好的查询性能。
Vehicle license plate recognition data provides a kind of traffic monitoring data that is a large spatial-temporal stream with fixed positions. Similar trajectory queries of such data face several problems. This paper presents a similar trajectory query method based on site companions with multistage task parallelization based on the MapReduce computing model. This method gives more efficient similar trajectory queries in a distributed computing environment for massive license plate recognition data. Tests show that this method can correctly query similar trajectories more efficiently than traditional stand-alone methods based on tests with almost ten million real vehicle license plate data points.
作者
赵卓峰
卢帅
韩燕波
ZHAO Zhuofeng LU Shuai HAN Yanbo(Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing 100144, China)
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第2期220-224,共5页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金重点项目(61033006)
北京市自然科学基金项目(4162021)
关键词
相似轨迹
车牌识别数据
点伴随
多级任务并行
similar trajectory
vehicle license plate recognition data
site companion
multistage task parallelization
