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Unsupervised Vehicle Re-Identification via Raw UAV Videos

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14356))

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Abstract

For matching vehicles across different camera views, vehicle Re-Identification has made great progress in supervised learning. However, supervised approach would require extensive manual labeling which is costly and unfeasible for large-scale vehicle Re-ID dataset. Therefore, we propose an unsupervised method to overcome the difficulty of vehicle ID labeling. Inspired by self-supervised methods in object tracking, we utilize self-supervised tracker to associate vehicle images in each unlabeled raw videos. We also utilize object sequence clustering method to associate vehicles from different videos and ensure the quality of the predicted pseudo labels. Based on these vehicle images and predicted labels discriminative vehicle features can be learned. In this paper, we construct a large-scale Unmanned Aerial Vehicle (UAV) vehicle video dataset to facilitate the study of video-based unsupervised vehicle Re-ID. Extensive experiments show that our method is effective and achieves competitive performance compared with recent unsupervised works. In addition, using the data obtained by the proposed method as the pre-training data can further improve the performance of the fully-supervised methods.

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References

  1. Bansal, A., Ma, S., Ramanan, D., Sheikh, Y.: Recycle-gan: Unsupervised video retargeting. In: ECCV (2018)

    Google Scholar 

  2. Chen, H., Wang, Y., Lagadec, B., Dantcheva, A., Bremond, F.: Learning invariance from generated variance for unsupervised person re-identification. IEEE Trans. Pattern Analysis Mach. Intell. (2022)

    Google Scholar 

  3. Cho, Y., Kim, W.J., Hong, S., Yoon, S.E.: Part-based pseudo label refinement for unsupervised person re-identification. In: CVPR, pp. 7298–7308 (2022)

    Google Scholar 

  4. Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: ICCV (2019)

    Google Scholar 

  5. Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person reidentification. In: CVPR (2018)

    Google Scholar 

  6. Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.: Temporal cycle-consistency learning. In: CVPR (2019)

    Google Scholar 

  7. Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Huang, T.S.: Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In: ICCV (2019)

    Google Scholar 

  8. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)

    Google Scholar 

  9. Ge, Y., Chen, D., Li, H.: Mutual mean teaching pseudo label refinery for unsupervised domain adaption on person reid. In: ICLR (2020)

    Google Scholar 

  10. He, B., Li, J., Zhao, Y., Tian, Y.: Part-regularized near-duplicate vehicle re-identification. In: CVPR (2019)

    Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  12. Kulkarni, N., Gupta, A., Tulsiani, S.: Canonical surface mapping via geometric cycle consistency. In: ICCV (2019)

    Google Scholar 

  13. Lai, Z., Lu, E., Xie, W.: Mast: A memory-augmented self-supervised tracker. In: CVPR (2020)

    Google Scholar 

  14. Li, M., Liu, J., Zheng, C., Huang, X., Zhang, Z.: Exploiting multi-view part-wise correlation via an efficient transformer for vehicle re-identification. IEEE Trans. Multimedia 25, 919–929 (2023)

    Article  Google Scholar 

  15. Li, Y., Fu, C., Ding, F., Huang, Z., Lu, G.: Autotrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. In: CVPR (2020)

    Google Scholar 

  16. Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: AAAI (2019)

    Google Scholar 

  17. Liu, H., Tian, Y., Yang, Y., Pang, L., Huang, T.: Deep relative distance learning: Tell the difference between similar vehicles. In: CVPR (2016)

    Google Scholar 

  18. Liu, W.: SSD: Single shot multibox detector. In: ECCV (2016)

    Google Scholar 

  19. 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: CVPR (2019)

    Google Scholar 

  20. Meng, D., et al.: Parsing-based view-aware embedding network for vehicle re-identification. In: CVPR (2020)

    Google Scholar 

  21. Qi, L., Wang, L., Huo, J., Zhou, L., Shi, Y., Gao, Y.: A novel unsupervised camera-aware domain adaptation framework for person re-identification. In: ICCV (2019)

    Google Scholar 

  22. Qian, W., Luo, H., Peng, S., Wang, F., Chen, C., Li, H.: Unstructured feature decoupling for vehicle re-identification. In: ECCV (2022). https://doi.org/10.1007/978-3-031-19781-9_20

  23. Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 549–565. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_33

    Chapter  Google Scholar 

  24. Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: CVPR (2017)

    Google Scholar 

  25. Song, L., et al.: Unsupervised domain adaptive re-identification: Theory and practice. In: PR (2020)

    Google Scholar 

  26. Teng, S., Liu, X., Zhang, S., Huang, Q.: SCAN: spatial and channel attention network for vehicle re-identification. In: Hong, R., Cheng, W.-H., Yamasaki, T., Wang, M., Ngo, C.-W. (eds.) PCM 2018. LNCS, vol. 11166, pp. 350–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00764-5_32

    Chapter  Google Scholar 

  27. Teng, S., Zhang, S., Huang, Q., Sebe, N.: Multi-view spatial attention embedding for vehicle re-identification. In: TCSVT (2020)

    Google Scholar 

  28. Teng, S., Zhang, S., Huang, Q., Sebe, N.: Viewpoint and scale consistency reinforcement for UAV vehicle re-identification. Int. J. Comput. Vision 129, 719–735 (2021)

    Article  Google Scholar 

  29. Wang, N., Song, Y., Ma, C., Zhou, W., Liu, W., Li, H.: Unsupervised deep tracking. In: CVPR (2019)

    Google Scholar 

  30. Wang, P., et al.: Vehicle re-identification in aerial imagery: Dataset and approach. In: ICCV (2019)

    Google Scholar 

  31. Wang, X., Jabri, A., Efros, A.A.: Efros. Learning correspondence from the cycle-consistency of time. In: CVPR (2019)

    Google Scholar 

  32. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: CVPR (2018)

    Google Scholar 

  33. Wu, A., Zheng, W.S., Lai, J.H.: Unsupervised person re-identification by camera-aware similarity consistency learning. In: ICCV (2019)

    Google Scholar 

  34. Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In: CVPR (2017)

    Google Scholar 

  35. Yang, F., Fan, H., Chu, P., Blasch, E., Ling, H.: Clustered object detection in aerial images. In: ICCV (2019)

    Google Scholar 

  36. Yu, H., et al.: The unmanned aerial vehicle benchmark: object detection, tracking and baseline. Int. J. Comput. Vision 128(5), 1141–1159 (2019). https://doi.org/10.1007/s11263-019-01266-1

    Article  Google Scholar 

  37. Yu, H., et al.: The unmanned aerial vehicle benchmark: Object detection, tracking and baseline. IJCV 128(5), 1141–1159 (2020)

    Article  MathSciNet  Google Scholar 

  38. Zach, C., Klopschitz, M., Pollefeys, M.: Disambiguating visual relations using loop constraints. In: CVPR (2010)

    Google Scholar 

  39. Zhang, X., et al.: Implicit sample extension for unsupervised person re-identification. In: CVPR, pp. 7359–7368 (2022)

    Google Scholar 

  40. Zhong, Z., Zheng, L., Cao, D., Li, S.: Reranking person re-identification with k-reciprocal encoding. In: CVPR (2017)

    Google Scholar 

  41. Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., Efros, A.A.: Learning dense correspondence via 3D-guided cycle consistency. In: CVPR (2016)

    Google Scholar 

  42. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)

    Google Scholar 

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Acknowledgements

This work is supported in part by The National Natural Science Foundation of China (62202061); Beijing Natural Science Foundation (4232025); R &D Program of Beijing Municipal Education Commission (KM202311232002).

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Authors and Affiliations

  1. Beijing Information Science and Technology University, Beijing, China

    Shangzhi Teng

  2. Beijing Special Engineering Design and Research Institute, Beijing, China

    Tingting Dong

Authors
  1. Shangzhi Teng
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  2. Tingting Dong
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Corresponding author

Correspondence to Shangzhi Teng .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Teng, S., Dong, T. (2023). Unsupervised Vehicle Re-Identification via Raw UAV Videos. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14356. Springer, Cham. https://doi.org/10.1007/978-3-031-46308-2_30

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  • DOI: https://doi.org/10.1007/978-3-031-46308-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46307-5

  • Online ISBN: 978-3-031-46308-2

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