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Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Author

Listed:
  • Yixian Chen

    (School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, China)

  • Zhaocheng He

    (School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, China)

Abstract

Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.

Suggested Citation

  • Yixian Chen & Zhaocheng He, 2020. "Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3074-:d:344296
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    References listed on IDEAS

    as
    1. Haiyang Yu & Shuai Yang & Zhihai Wu & Xiaolei Ma, 2018. "Vehicle trajectory reconstruction from automatic license plate reader data," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    2. Ran, Bin & Tan, Huachun & Wu, Yuankai & Jin, Peter J., 2016. "Tensor based missing traffic data completion with spatial–temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 54-63.
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