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A framework for cloned vehicle detection
Frontiers of Computer Science ( IF 4.2 ) Pub Date : 2020-01-03 , DOI: 10.1007/s11704-019-9005-4
Minxi Li , Jiali Mao , Xiaodong Qi , Cheqing Jin

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy. It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately, and further explore the motives through discerning the behaviors of cloned vehicles. The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles, which opens up a new opportunity for cloned vehicle detection. Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold. In this paper, we propose a two-phase framework, called CVDF, to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number. In the detection phase, cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood. In the behavior analysis phase, consider the traces of vehicles that uses the same license plate will be mixed together, we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns. The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’ behavior effectively. Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.

中文翻译:

克隆车辆检测的框架

猖clone的克隆车辆违法行为严重损害了运输管理,公共安全乃至世界经济。它需要一种有效的检测机制来准确识别带有假车牌的车辆,并通过辨别克隆车辆的行为来进一步探究动机。该城市无处不在的检查点一直在收集过往车辆的行驶信息,这为克隆车辆的检测开辟了新的机会。现有的检测方法由于使用固定的速度阈值而不能有效地检测到克隆的车辆。在本文中,我们提出了一个称为CVDF的两阶段框架,以检测克隆的车辆并区分使用相同车牌号的车辆的行为模式。在检测阶段,基于从历史轨迹提取的速度阈值和本地邻域内的行为异常分析来识别克隆的车辆。在行为分析阶段,考虑将使用相同车牌的车辆的痕迹混合在一起,我们的目的是通过匹配基于度的聚类,然后提取频繁的时间行为模式。实际数据的实验结果表明,CVDF框架具有较高的检测精度,可以有效地揭示克隆车辆的行为。我们的建议为交通管理部门解决克隆车辆犯罪提供了科学依据。
更新日期:2020-01-03
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