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国家自然科学基金（61672042）；北京市市委组织部、北京市优秀人才培养资助，青年骨干个人项目；北方工业大学“人才强校计划”青年拔尖人才培育计划
- ZHU Mei-Ling
ZHU Mei-Ling
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China;Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data(North China University of Technology), Beijing 100144, China;Cloud Computing Research Center, North China University of Technology, Beijing 100144, China
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National Natural Science Foundation of China (61672042); Program for Youth Backbone Individual, Beijing Municipal Party Committee Organization Department; Training Plan of Top Young Talent in North China University of Technology
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针对伴随车辆检测这一新兴的智能交通应用，在一种特殊的流式时空大数据——车牌识别流式大数据（ANPR）下，重新定义了Platoon伴随模式，提出PlatoonFinder算法，即时地在车牌识别数据流上挖掘Platoon伴随模式.主要贡献包括：第一，将Platoon伴随模式发现问题映射为数据流上的带有时空约束的频繁序列挖掘问题，与传统频繁序列挖掘算法仅考虑序列元素之间位置关系不同，该算法能够在频繁序列挖掘的过程中有效处理序列元素之间复杂的时空约束关系；第二，该算法融入了伪投影等性能优化技术，针对数据流的特点进行了性能优化，能够有效应对车牌识别流式大数据的速率和规模，从而实现车辆Platoon伴随模式的即时发现.通过在真实车牌识别数据集上的实验分析表明：PlatoonFinder算法的平均延时显著低于经典的Aprior和PrefixSpan等频繁模式挖掘算法，也低于真实情况下交通摄像头的车牌识别最小时间间隔.因此，所提出的算法可以有效地发现伴随车辆组及其移动模式.
Companion vehicle discovery is a newly emerging intelligent transportation application. Aiming at it, this paper redefines the Platoon companion pattern over a special type of spatio-temporal data stream, or ANPR (automatic number plate recognition data). Accordingly, a PlatoonFinder algorithm is also proposed to mine Platoon companions over ANPR data stream instantly. First, Platoon discovery problem is transformed into frequent sequence mining problem with customized spatio-temporal constraints. Compared to traditional frequent sequence mining algorithms, this new algorithm can effectively handle complex spatio-temporal relationships among sequence elements rather than their positions. Second, the new algorithm also integrates several optimization techniques such as pseudo projection to greatly improve the efficiency. It can efficiently deal with high speed and large scale ANPR data stream so as to instantly discover Platoon companions. Experiments show that the latency of the algorithm is significantly lower than classic frequent pattern mining algorithms including Apriori and Prefixspan. Furthermore, it is also lower than the minimum time interval between any two real ANPR data records. Hence, the proposed algorithm can discover Platoon companions effectively and efficiently.
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朱美玲,刘晨,王雄斌,韩燕波.基于车牌识别流数据的车辆伴随模式发现方法.软件学报,2017,28(6):1498-1515
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- 收稿日期:2016-05-07
- 最后修改日期:2016-07-15
- 在线发布日期: 2017-02-21
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