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Fine-Grained Truck Re-identification: A Challenge

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Abstract

In intelligent transportation and smart city, truck re-identification (Re-ID) is a crucial task in controlling traffic violations of laws and regulations, especially in the absence of satellite positioning and license plate information. There are many specific fine-grained types in trucks compared to common person and vehicle Re-ID, which hinders the direct application of person and vehicle Re-ID methods to truck Re-ID. In this work, we contribute a new truck image dataset, named Truck-ID, for truck Re-ID specifically. The dataset contains 32,353 images of trucks from 7 monitoring sites of real traffic surveillance, including 13,137 license plate IDs. According to the difficulty of truck Re-ID, the gallery of Truck-ID dataset is further divided into three sub-datasets to evaluate the quality of different truck Re-ID models more comprehensively. Furthermore, we propose an effective Double Granularity Network (DGN) for truck Re-ID, which considers both global and local features of truck by focusing on truck head and body separately. Experiments show that DGN can effectively integrate global and local features to achieve robust fine-grained truck Re-ID. Our work provides a benchmark dataset for truck Re-ID and a baseline network for both research and industrial communities. The Truck-ID dataset and DGN codes are available at: https://pan.baidu.com/s/18Vc6NOiipGLLvcKj8U75Hw. Although the proposed DGN is relatively simple and easy to implement, it is effective in learning discriminative features of trucks and has remarkable performance in targeting truck re-identification. The Truck-ID dataset we made can promote the development of re-identification in the truck field.

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Data Availability

Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies (The Truck-ID dataset and DGN codes are available at: https://pan.baidu.com/s/18Vc6NOiipGLLvcKj8U75Hw).

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Acknowledgements

The authors would like to thank the Editor and anonymous reviewers for their valuable comments and suggestions, which were helpful in improving the paper.

Funding

This work was supported in part by NSFC Key Project of International (Regional) Cooperation and Exchanges (No.61860206004) and National Natural Science Foundation of China (No.61976004).

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Authors and Affiliations

  1. IMIS Laboratory of Anhui Province, Anhui Provincial Key Lab of Multimodal Cognitive Computation, Key Lab of ICSP of MOE, School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Si-Bao Chen, Zi-Han Lin & Bin Luo

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Chris H. Q. Ding

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  1. Si-Bao Chen
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Correspondence to Si-Bao Chen.

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Chen, SB., Lin, ZH., Ding, C.H.Q. et al. Fine-Grained Truck Re-identification: A Challenge. Cogn Comput 15, 1947–1960 (2023). https://doi.org/10.1007/s12559-023-10162-3

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