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A framework for cloned vehicle detection

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Abstract

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.

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Acknowledgements

Our research was supported by NSFC (Grant Nos. U1501252, U1711262, 61702423 and U1811264).

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Authors and Affiliations

  1. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Minxi Li, Jiali Mao, Xiaodong Qi & Cheqing Jin

Authors
  1. Minxi Li
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  2. Jiali Mao
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  3. Xiaodong Qi
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  4. Cheqing Jin
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Corresponding author

Correspondence to Jiali Mao.

Additional information

Minxi Li is currently working toward the graduate degree in the School of Data Science and Engineering, East China Normal University, China. Her research interests include spatio-temporal data mining and location-based services.

Jiali Mao is a research professor on computer science at East China Normal University, China. She received the PhD degree in East China Normal University, China. Her main research interests include big data analysis and location-based services.

Xiaodong Qi is currently working toward the PhD degree at the School of Data Science and Engineering, East China Normal University, China. His research interests include scientific data management and block chain.

Cheqing Jin is a professor on computer science at East China Normal University, China. He received Excellent Young Teacher Award by Fok Ying Tung Education Foundation. His main research interests include: streaming data management, location-based services and uncertain data management.

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Li, M., Mao, J., Qi, X. et al. A framework for cloned vehicle detection. Front. Comput. Sci. 14, 145609 (2020). https://doi.org/10.1007/s11704-019-9005-4

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  • DOI: https://doi.org/10.1007/s11704-019-9005-4

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