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Fake license plate recognition in surveillance videos

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Abstract

Fake license plate (FLP) recognition aims to identify modified, defaced or forged license plates in traffic videos and images. The recognition result, that is, the vehicle with an FLP, is important information that helps to identify an illegal vehicle in an intelligent transport system. In this paper, we propose a novel framework for FLP recognition using two deep neural networks: VMMR-Net and VColor-Net. VMMR-Net is used to recognize the vehicle manufacturer and model, whereas VColor-Net is used to recognize the color of the vehicle. Both the two networks can achieve a relative high recognition accuracy rate (with accuracy 97.18% and 93.78%) in natural scenes and ensure low computational complexity. Furthermore, we construct a dataset that contains 20,761 region of interest images that belong to 81 VMM classes from a surveillance system. Without using a segmentation algorithm, the network learns how to locate the frontal area of a vehicle and then recognize its manufacturer and model and color directly. The experimental results show that the proposed method demonstrates good performance on our traffic image dataset.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610000, People’s Republic of China

    Wei Pan, Xin Zhou & Yuanyuan Chen

  2. University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Tao Zhou

Authors
  1. Wei Pan
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  2. Xin Zhou
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  3. Tao Zhou
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  4. Yuanyuan Chen
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Correspondence to Yuanyuan Chen.

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This work was supported by Department of Science and Technology of Sichuan Province, China (Grant Nos. 20ZDYF2060 and 2021YFQ0010).

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Cite this article

Pan, W., Zhou, X., Zhou, T. et al. Fake license plate recognition in surveillance videos. SIViP 17, 937–945 (2023). https://doi.org/10.1007/s11760-022-02264-6

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  • DOI: https://doi.org/10.1007/s11760-022-02264-6

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