Abstract
With the increasing usage of massive surveillance data, vehicle re-identification (re-ID) has become a hot topic in the computer vision community. Vehicle re-ID is a challenging problem due to the viewpoint variation, i.e. the different views greatly affect the visual appearance of a vehicle. To handle this problem, we propose an end-to-end framework called Keypoint Guided Multi-Scale Reasoning Network (MRNet) to infer multi-view vehicle features from a one-view image. In our proposed framework, besides the global branch, we learn multi-view vehicle information by introducing a local branch, which leverages different vehicle segments to do relational reasoning. MRNet can infer the latent whole vehicle feature by increasing the semantic similarity between incomplete vehicle segments. MRNet is evaluated on two benchmarks (VeRi-776 and VehicleID) and the experimental results show that our framework has achieved competitive performance with the state-of-the-art methods. On the more challenging dataset VeRi-776, we achieve 72.0% in mAP and 92.4% in Rank-1. Our code is available at https://github.com/panmt/MRNet_for_vehicle_reID.
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References
Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circ. Syst. Video Technol. 23(2), 311–325 (2013)
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, pp. 6853–6860 (2018)
He, B., Li, J., Zhao, Y., Tian, Y.: Part-regularized near-duplicate vehicle re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3992–4000 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J.C., Chellappa, R.: A dual-path model with adaptive attention for vehicle re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
Lalimi, M.A., Ghofrani, S., Mclernon, D.: A vehicle license plate detection method using region and edge based methods. Comput. Electr. Eng. 39(3), 834–845 (2013)
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 (CVPR) (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 (2018)
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
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/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond. In: ICLR (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Tang, Z., et al.: PAMTRI: pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Wang, Z., et al.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 379–387 (2017)
Wei, X.-S., Zhang, C.-L., Liu, L., Shen, C., Wu, J.: Coarse-to-fine: a RNN-based hierarchical attention model for vehicle re-identification. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11362, pp. 575–591. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20890-5_37
Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhou, 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)
Zhou, Y., Shao, L.: Cross-view GAN based vehicle generation for re-identification. In: Proceedings of the British Machine Vision Conference (BMVC) (2017)
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Pan, M., Zhu, X., Li, Y., Qian, J., Liu, P. (2020). MRNet: A Keypoint Guided Multi-scale Reasoning Network for Vehicle Re-identification. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_54
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