计算机应用 ›› 2017, Vol. 37 ›› Issue (1): 153-158.DOI: 10.11772/j.issn.1001-9081.2017.01.0153

• 2016年全国开放式分布与并行计算学术年会(DPCS2016)论文 • 上一篇    下一篇

面向套牌甄别的流式计算系统

乔通1,2, 赵卓峰1,2, 丁维龙1,2   

  1. 1. 北方工业大学 数据工程研究院, 北京 100144;
    2. 大规模流数据集成与分析技术北京市重点实验室(北方工业大学), 北京 100144
  • 收稿日期:2016-07-25 修回日期:2016-08-08 出版日期:2017-01-10 发布日期:2017-01-09
  • 通讯作者: 乔通
  • 作者简介:乔通(1991-),男,山东泰安人,硕士研究生,主要研究方向:实时数据处理、智能交通;赵卓峰(1977-),男,山东济南人,副研究员,博士,CCF高级会员,主要研究方向:云计算、海量数据处理、智能交通;丁维龙(1983-),男,山东泰安人,讲师,博士,CCF会员,主要研究方向:实时数据处理、分布式系统。
  • 基金资助:
    北京市自然科学基金资助项目(4162021),北京市教育委员会科技计划面上项目(KM2015_10009007);北京市优秀人才培养资助青年骨干个人项目(2014000020124G011)。

Stream computing system for monitoring copy plate vehicles

QIAO Tong1,2, ZHAO Zhuofeng1,2, DING Weilong1,2   

  1. 1. Institute of Data Engineering, North China University of Technology, Beijing 100144, China;
    2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data(North China University of Technology), Beijing 100144, China
  • Received:2016-07-25 Revised:2016-08-08 Online:2017-01-10 Published:2017-01-09
  • Supported by:
    This work is partially supported by the Beijing Municipal Natural Science Foundation (4162021), the R&D General Program of Beijing Education Commission (KM2015_10009007), the Key Young Scholars Foundation for the Excellent Talents of Beijing (2014000020124G011).

摘要: 套牌车的甄别具有时效性约束。针对现有计算检测方法中所出现的精度低、响应慢等局限,提出了一种基于实时车牌识别(ANPR)数据流的套牌车流式并行检测方法,设计了基于路段阈值表和时间滑动窗口的套牌计算模型,能够实时地甄别出交通数据流中的套牌嫌疑车。在Storm环境下,利用某市真实交通数据集模拟成实时交通流数据进行实验和评估,实验结果表明计算的准确率达到98.7%,并且一条车牌识别数据的处理时间为毫秒级。最后,在该计算模型基础上实现了套牌车稽查防控系统,能实时甄别并展现出当前时刻城市交通网中出现的所有套牌嫌疑车。

关键词: 套牌车, 车牌识别, 流式计算, 实时性, 阈值表, Storm

Abstract: The screening of the copy plate vehicles has timeliness, and the existing detection approaches for copy plate vehicles have slow response and low efficiency. In order to improve the real-time response ability, a new parallel detection approach, called stream computing, based on real-time Automatic Number Plate Recognition (ANPR) data stream, was proposed. To deal with the traffic information of the road on time, and plate vehicles could be timely feedback and controlled, a stream calculation model was implemented by using the threshold table of road travel time and the time sliding window, which could access real-time traffic data stream to calculate copy plate vehicles. On the platform of Storm, this system was designed and implemented. The calculation model was verified on a real-time data stream which was simulated by the real ANPR dataset of a city. The experimental results prove that a piece of license plate recognition data can be dealt with in milliseconds from the time of arrival to the calculation completion, also, the calculation accuracy is 98.7%. Finally, a display system for copy vehicles was developed based on this calculation model, which can show the copy plate vehicles from the road network on the current moment.

Key words: copy plate vehicle, Automatic Number Plate Recognition (ANPR), stream computing, real-time, threshold table, Storm

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