Abstract
Vehicle re-identification (Re-ID) is the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various appearance-based approaches focusing only on global features or exploring local features to obtain more subtle details which can alleviate the subtle inter-instance problem. However, few emphasize the spatial geometrical structure relationship among local regions or between the global region and local regions. To explore above-mentioned spatial structure relationship, this paper proposes a hierarchical spatial structural graph convolutional network (HSS-GCN) for vehicle Re-ID, in which we firstly construct a hierarchical spatial structural graph with the global region and local regions as nodes and a two-hierarchy relationship as edges, and later learning discriminative structure features with a GCN module under the constraints of metric learning. To augment the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.
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References
Bai, Y., Lou, Y., Gao, F., Wang, S., Wu, Y., Duan, L.Y.: Group-sensitive triplet embedding for vehicle reidentification. IEEE Trans. Multimed. 20(9), 2385–2399 (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Guo, H., Zhao, C., Liu, Z., Wang, J., Lu, H.: Learning coarse-to-fine structured feature embedding for vehicle re-identification. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
He, B., Li, J., Zhao, Y., Tian, Y.: Part-regularized near-duplicate vehicle re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3997–4005 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)
Liu, H., Tian, Y., Yang, Y., Pang, L., Huang, T.: Deep relative distance learning: Tell the difference between similar vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2167–2175 (2016)
Liu, X., Zhang, S., Huang, Q., Gao, W.: Ram: a region-aware deep model for vehicle re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2018)
Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2016)
Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 869–884. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_53
Liu, X., et al.: Social relation recognition from videos via multi-scale spatial-temporal reasoning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3566–3574 (2019)
Liu, X., Liu, W., Zheng, J., Yan, C., Mei, T.: Beyond the parts: Learning multi-view cross-part correlation for vehicle re-identification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 907–915 (2020)
Lou, Y., Bai, Y., Liu, J., Wang, S., Duan, L.: Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3235–3243 (2019)
Shen, Y., Li, H., Yi, S., Chen, D., Wang, X.: Person re-identification with deep similarity-guided graph neural network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 486–504 (2018)
Shen, Y., Xiao, T., Li, H., Yi, S., Wang, X.: Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1900–1909 (2017)
Wang, Z., et al.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 379–387 (2017)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv:1801.07455 (2018)
Yang, J., Zheng, W.S., Yang, Q., Chen, Y.C., Tian, Q.: Spatial-temporal graph convolutional network for video-based person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3289–3299 (2020)
Zhang, Y., Liu, D., Zha, Z.J.: Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 1386–1391. IEEE (2017)
Zhou, Y., Shao, L.: Cross-view gan based vehicle generation for re-identification. BMVC. 1, 1–12 (2017)
Zhouy, Y., Shao, L.: Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6489–6498 (2018)
Acknowledgement
This work was supported by the Fundamental Research Funds for the Central Universities (No. 2020JBM403), the Beijing Natural Science Foundation (Grants No. 4202057, No. 4202058, 4202060), National Natural Science Foundation of China (No. 62072027, 61872032), and the Ministry of Education - China Mobile Communications Corporation Foundation (No. MCM20170201).
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Xu, Z., Wei, L., Lang, C., Feng, S., Wang, T., Bors, A.G. (2021). HSS-GCN: A Hierarchical Spatial Structural Graph Convolutional Network for Vehicle Re-identification. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_32
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