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Cross-view vehicle re-identification based on graph matching

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Abstract

Cross-view identification is one of the challenges in the task of vehicle re-identification. Because of the different shapes, structures, and surface area, changes in viewing angle have a greater impact on vehicle re-ID than on person re-ID. Previous work mainly focused on generating other viewing angle features through a single view feature of the vehicle to achieve cross-view identification. This paper proposes a systematic framework to realize the alignment and discrimination of key features by learning high-order relationships and topological information. First, the feature extraction module (FEM) is applied to extract local and global features of the vehicle. Then,the graph convolution module (GCM) integrates the topological structure information between the key points of the vehicle into the local features to compensate for the unexposed key points caused by the blind zone. Finally, the graph matching module(GMM) robustly aligns the key features between the two graphs and calculates their similarity. Experimental results show that our proposed method VGM has competitive results with the existing state-of-the-art methods on benchmark datasets VeRi-776 and VARI.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 62006242 and 62106258.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, Heilongjiang, China

    Chao Zhang & Hongbin Dong

  2. Defense Innovation Institute (DII), Academy of Military Sciences, Beijing, 100071, China

    Chule Yang & Baosong Deng

  3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Dayan Wu

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  1. Chao Zhang
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  2. Chule Yang
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  3. Dayan Wu
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  4. Hongbin Dong
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  5. Baosong Deng
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Correspondence to Chule Yang.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Zhang, C., Yang, C., Wu, D. et al. Cross-view vehicle re-identification based on graph matching. Appl Intell 52, 14799–14810 (2022). https://doi.org/10.1007/s10489-022-03349-y

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