Detection of fake plate vehicles based on traffic data stream

LI Min-xi; MAO Jia-li; YUAN Pei-sen; JIN Che-qing

doi:10.3969/j.issn.1000-5641.2018.02.007

Issue 2

Mar. 2018

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LI Min-xi, MAO Jia-li, YUAN Pei-sen, JIN Che-qing. Detection of fake plate vehicles based on traffic data stream[J]. Journal of East China Normal University (Natural Sciences), 2018, (2): 63-76, 100. doi: 10.3969/j.issn.1000-5641.2018.02.007

Citation:

LI Min-xi, MAO Jia-li, YUAN Pei-sen, JIN Che-qing. Detection of fake plate vehicles based on traffic data stream[J]. Journal of East China Normal University (Natural Sciences), 2018, (2): 63-76, 100. doi: 10.3969/j.issn.1000-5641.2018.02.007

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Detection of fake plate vehicles based on traffic data stream

doi: 10.3969/j.issn.1000-5641.2018.02.007

1.
School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China
2.
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
3.
College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China

Received Date: 2017-06-19
Publish Date: 2018-03-25

Abstract

Abstract

Since vehicles using fake plate violate traffic laws and regulations, infringe the rights of legal license owners and harm social benefits, it is therefore of great urgency to solve this social problem. In current detection methods, unified speed threshold is used to detect fake plate vehicles. Once an inappropriate threshold was set, it would lead to misjudgements and omissions, and thereby results in a low judgement accuracy. To solve this problem, we propose a two-phase fake plate vehicles detection framework. In the offline part, we train a distributed speed model based on sparse traffic trajectory data to calculate time-dependent thresholds for different roads. In the online part, we apply a sliding-window method to monitor whether a car is an outlier. If a car is continually detected as an outlier, it will be judged as a fake, and vice versa. We then use a real dataset to evaluate our method. The results demonstrate negative influences of noise data can be avoided by our method, so the accuracy of fake plate vehicles detection can be improved significantly.
- fake plate vehicle,
- anomaly detection,
- speed distribution,
- sliding window

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References(15)

References

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