Abstract
Recently, the rapid development of vehicle re-identification (ReID) technology has facilitated the construction of intelligent transport systems. Mainstream ReID methods rely on the fusion of global and local features. In the global feature extraction, the channel attention modules are usually exploited in the network, most of which only focus on the channels’ importance and ignore the interactions among channels. In the local feature extraction, the additional annotation-based local feature extraction methods can focus on local information and improve the model’s performance but increase the workload of the data annotation and reduce the generalizability of the model. In this article, we put forward a new ReID Algorithm called CCSAM-LL. Firstly, a plug-and-play module based on channel correlation self-attention called CCSAM is introduced, which focuses on channel relevance and improves the characterization of global features. Secondly, we propose an Lstm-based loss, named LstmLocal loss, which takes into account local features without additional annotation. LstmLocal loss is trained with Triplet Hard loss and ID loss to improve the model’s ability to capture detailed features and accuracy in the retrieval task. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on the challenging dataset VeRi776. Specifically, our approach achieves 83.18% mAP, 98.79% Rank5, and 48.83% mINP. The model is available at https://gitee.com/qitiantian128/ccsam-ll.
This work was supported by National Key Research and Development Program of China 2018YFB2101300 and the Dean’s Fund of Engineering Research Center of Software/Hardware Co-design Technology and Application, Ministry of Education, East China Normal University.
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References
Bai, Y., et al.: Disentangled feature learning network for vehicle re-identification. In: IJCAI, pp. 474–480 (2020)
Chen, G., Zhang, T., Lu, J., Zhou, J.: Deep meta metric learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9547–9556 (2019)
Chen, H., Lagadec, B., Bremond, F.: Partition and reunion: a two-branch neural network for vehicle re-identification. In: CVPR Workshops, pp. 184–192 (2019)
Chen, T.-S., Liu, C.-T., Wu, C.-W., Chien, S.-Y.: Orientation-aware vehicle re-identification with semantics-guided part attention network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 330–346. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_20
Guo, H., Zhu, K., Tang, M., Wang, J.: Two-level attention network with multi-grain ranking loss for vehicle re-identification. IEEE Trans. Image Process. 28, 4328–4338 (2019)
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) (2019)
He, L., Liao, X., Liu, W., Liu, X., Cheng, P., Mei, T.: Fastreid: a pytorch toolbox for real-world person re-identification, vol. 1, no. 6. ar**v preprint ar**v:2006.02631 (2020)
He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: Transreid: transformer-based object re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15013–15022 (2021)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. ar**v preprint ar**v:1703.07737 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
**, X., Lan, C., Zeng, W., Chen, Z.: Uncertainty-aware multi-shot knowledge distillation for image-based object re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11165–11172 (2020)
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/CVF International Conference on Computer Vision, pp. 6132–6141 (2019)
Khorramshahi, P., Peri, N., Chen, J., Chellappa, R.: The devil is in the details: self-supervised attention for vehicle re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 369–386. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_22
Li, M., Huang, X., Zhang, Z.: Self-supervised geometric features discovery via interpretable attention for vehicle re-identification and beyond. In: International Conference on Computer Vision (2021)
Liu, H., Tian, Y., Wang, Y., Lu, P., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (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
Meng, D., et al.: Parsing-based view-aware embedding network for vehicle re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7103–7112 (2020)
Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via ibn-net. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 464–479 (2018)
Qian, J., Jiang, W., Luo, H., Yu, H.: Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification. Measure. Sci. Technol. 31(9), 095401 (2020)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Sun, W., Dai, G., Zhang, X., He, X., Chen, X.: Tbe-net: a three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification. IEEE Trans. Intell. Transp. Syst. (2021)
Sun, Z., Nie, X., **, X., Yin, Y.: Cfvmnet: a multi-branch network for vehicle re-identification based on common field of view. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3523–3531 (2020)
Suprem, A., Pu, C.: Looking glamorous: vehicle re-id in heterogeneous cameras networks with global and local attention. ar**v preprint ar**v:2002.02256 (2020)
Varior, R.R., Shuai, B., Lu, J., Xu, D., Wang, G.: A siamese long short-term memory architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 135–153. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_9
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Bai, Y., Lou, Y., Gao, F., Wang, S., Wu, Y., Duan, L.Y.: Group-sensitive triplet embedding for vehicle reidentification. IEEE Trans. Multimedia 20(9), 2385–2399 (2018)
Ye, M., Shen, J., Lin, G., **ang, T., Shao, L., Hoi, S.C.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intelligence 44(6), 2872–2893 (2021)
Yi, Z., Ling, S.: Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Zhang, X., Zhang, R., Cao, J., Gong, D., You, M., Shen, C.: Part-guided attention learning for vehicle instance retrieval. IEEE Trans. Intell. Transp. Syst. 23, 3048–3060 (2020)
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Qi, T., Qiu, S., Sun, L., Liu, Z., Chen, M., Lyu, Y. (2022). A Vehicle Re-ID Algorithm Based on Channel Correlation Self-attention and Lstm Local Information Loss. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_36
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