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29-03-2023 | Regular Paper

View-aware attribute-guided network for vehicle re-identification

Authors: Saifullah Tumrani, Wazir Ali, Rajesh Kumar, Abdullah Aman Khan, Fayaz Ali Dharejo

Published in: Multimedia Systems | Issue 4/2023

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Abstract

Vehicle re-identification is one of the essential application of urban surveillance. Due to enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles. Additionally, varying illumination and complex environments create significant hurdles for the existing methods to re-identify vehicles. We present a multi-guided learning method in this paper that uses multi-attribute and view point information, while also enhancing the robustness of feature extraction. The multi-attribute sub-network learns discriminative features like, i.e. color and type of vehicle. Moreover, the view predictor network adds extra information to the feature embedding and To validate the effectiveness of our framework, experiments on two benchmark datasets VeRi-776 and VehicleID are conducted. Experimental results illustrate our framework achieved comparative performance.

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Tumrani, S., Parivish, P., Khan, A.A., Ali, W.: Two stream pose guided network for vehicle re-identification. In: 2021 3rd International Conference on Image Processing and Machine Vision (IPMV). IPMV 2021, pp. 11–16. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3469951.3469954

Zhou, Y., Shao, L.: Cross-view GAN based vehicle generation for re-identification. In: British Machine Vision Conference 2017, BMVC 2017, London, UK, September 4-7, 2017. BMVA Press (2017). https://www.dropbox.com/s/6i9or0tycozn7zj/0039.pdf?dl=1

Tumrani, S., Deng, Z., Lin, H., Shao, J.: Partial attention and multi-attribute learning for vehicle re-identification. Pattern Recognit. Lett. 138, 290–297 (2020). https://doi.org/10.1016/j.patrec.2020.07.034CrossRef

Tumrani, S., Deng, Z., Khan, A.A., Ali, W.: PEVR: pose estimation for vehicle re-identification. In: Song, J., Zhu, X. (eds.) Web and Big Data-APWeb-WAIM 2019 International Workshops, KGMA and DSEA, Chengdu, China, August 1-3, 2019, Revised Selected Papers. Lecture Notes in Computer Science, vol. 11809, pp. 69–78. Springer (2019). https://doi.org/10.1007/978-3-030-33982-1_6

Watcharapinchai, N., Rujikietgumjorn, S.: Approximate license plate string matching for vehicle re-identification. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, Lecce, Italy, August 29–September 1, 2017, pp. 1–6 (2017)

Xiao, D., Zhang, L., Li, J., Li, J.: Robust license plate detection and recognition with automatic rectification. J. Electron. Imaging 30(1), 013002 (2021). https://doi.org/10.1117/1.JEI.30.1.013002CrossRef

Zhao, Y., Yu, Z., Li, X., Cai, M.: Chinese license plate image database building methodology for license plate recognition. J. Electron. Imaging 28(1), 013001 (2019). https://doi.org/10.1117/1.JEI.28.1.013001CrossRef

Zhuge, C., Peng, Y., Li, Y., Ai, J., Chen, J.: Attribute-guided feature extraction and augmentation robust learning for vehicle re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, June 14–19, 2020, pp. 2632–2637. Computer Vision Foundation/IEEE (2020). https://doi.org/10.1109/CVPRW50498.2020.00317

Quispe, R., Lan, C., Zeng, W., Pedrini, H.: Attributenet: attribute enhanced vehicle re-identification. CoRR (2021). arXiv:2102.03898

10.

Zhou, Y., Shao, L.: Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 6489–6498 (2018)

11.

Meng, D., Li, L., Liu, X., Li, Y., Yang, S., Zha, Z., Gao, X., Wang, S., Huang, Q.: Parsing-based view-aware embedding network for vehicle re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13–19, 2020, pp. 7101–7110. IEEE (2020). https://doi.org/10.1109/CVPR42600.2020.00713

12.

Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27-November 2, 2019, pp. 8281–8290. IEEE (2019). https://doi.org/10.1109/ICCV.2019.00837

13.

Zhou, X., Karpur, A., Luo, L., Huang, Q.: Starmap for category-agnostic keypoint and viewpoint estimation. In: Computer Vision-ECCV 2018-15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I, pp. 328–345 (2018)

14.

Tulsiani, S., Malik, J.: Viewpoints and keypoints. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pp. 1510–1519. IEEE Computer Society (2015). https://doi.org/10.1109/CVPR.2015.7298758

15.

Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 7103–7112 (2018)

16.

Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: IEEE International Conference on Multimedia and Expo, ICME 2016, Seattle, WA, USA, July 11-15, 2016, pp. 1–6 (2016)

17.

Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 2167–2175 (2016)

18.

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.) Computer Vision-ECCV 2016-14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II. Lecture Notes in Computer Science, vol. 9906, pp. 869–884. Springer (2016). https://doi.org/10.1007/978-3-319-46475-6_53

19.

Liu, X., Liu, W., Mei, T., Ma, H.: PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimed. 20(3), 645–658 (2018). https://doi.org/10.1109/TMM.2017.2751966CrossRef

20.

Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: Balcan, M., Weinberger, K.Q. (eds.) Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 507–516. JMLR.org (2016). http://proceedings.mlr.press/v48/liud16.html

21.

Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., Wang, X.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22–29, 2017, pp. 379–387 (2017)

22.

Li, Y., Li, Y., Yan, H., Liu, J.: Deep joint discriminative learning for vehicle re-identification and retrieval. In: 2017 IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, September 17–20, 2017, pp. 395–399. IEEE (2017). https://doi.org/10.1109/ICIP.2017.8296310

23.

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: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22–29, 2017, pp. 1918–1927. IEEE Computer Society (2017). https://doi.org/10.1109/ICCV.2017.210

24.

Wang, H., Peng, J., Chen, D., Jiang, G., Zhao, T., Fu, X.: Attribute-guided feature learning network for vehicle reidentification. IEEE Multimed. 27(4), 112–121 (2020). https://doi.org/10.1109/MMUL.2020.2999464CrossRef

25.

Wang, H., Jiang, G., Peng, J., Deng, R., Fu, X.: Towards adaptive consensus graph: multi-view clustering via graph collaboration. IEEE Trans. Multimed. (2022). https://doi.org/10.1109/TMM.2022.3212270CrossRef

26.

Wang, H., Peng, J., Jiang, G., Xu, F., Fu, X.: Discriminative feature and dictionary learning with part-aware model for vehicle re-identification. Neurocomputing 438, 55–62 (2021)CrossRef

27.

Wang, H., Wang, Y., Zhang, Z., Fu, X., Zhuo, L., Xu, M., Wang, M.: Kernelized multiview subspace analysis by self-weighted learning. IEEE Trans. Multimed. 23, 3828–3840 (2021)CrossRef

28.

Wang, H., Peng, J., Zhao, Y., Fu, X.: Multi-path deep cnns for fine-grained car recognition. IEEE Trans. Veh. Technol. 69(10), 10484–10493 (2020)CrossRef

29.

Wang, H., Peng, J., Chen, D., Jiang, G., Zhao, T., Fu, X.: Attribute-guided feature learning network for vehicle reidentification. IEEE Multimed. 27(4), 112–121 (2020)CrossRef

30.

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 2018, San Diego, CA, USA, July 23–27, 2018, pp. 1–6. IEEE Computer Society (2018). https://doi.org/10.1109/ICME.2018.8486589

31.

Yan, K., Tian, Y., Wang, Y., Zeng, W., Huang, T.: Exploiting multi-grain ranking constraints for precisely searching visually-similar vehicles. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22–29, 2017, pp. 562–570. IEEE Computer Society (2017). https://doi.org/10.1109/ICCV.2017.68

32.

Yang, W., Yan, Y., Chen, S.: Adaptive deep metric embeddings for person re-identification under occlusions. Neurocomputing 340, 125–132 (2019). https://doi.org/10.1016/j.neucom.2019.02.042CrossRef

33.

Zheng, A., Lin, X., Li, C., He, R., Tang, J.: Attributes guided feature learning for vehicle re-identification (2019)

34.

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. (eds.) Computer Vision-ECCV 2020-16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV. Lecture Notes in Computer Science, vol. 12359, pp. 369–386. Springer (2020). https://doi.org/10.1007/978-3-030-58568-6_22

35.

Tang, Z., Naphade, M., Liu, M., Yang, X., Birchfield, S., Wang, S., Kumar, R., Anastasiu, D.C., Hwang, J.: Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16–20, 2019, pp. 8797–8806. Computer Vision Foundation/IEEE (2019). https://doi.org/10.1109/CVPR.2019.00900

36.

Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20–25 June 2009, Miami, Florida, USA, pp. 248–255. IEEE Computer Society (2009). https://doi.org/10.1109/CVPR.2009.5206848

37.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27–30, 2016, pp. 770–778. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.90

38.

Huynh, S.V.: A strong baseline for vehicle re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2021, Virtual, June 19–25, 2021, pp. 4147–4154. Computer Vision Foundation/IEEE (2021). https://doi.org/10.1109/CVPRW53098.2021.00468

39.

Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015, pp. 2197–2206 (2015)

40.

Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7–13, 2015, pp. 1116–1124. IEEE Computer Society (2015). https://doi.org/10.1109/ICCV.2015.133

41.

Yang, L., Luo, P., Loy, C.C., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015, pp. 3973–3981 (2015)

42.

Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27–30, 2016, pp. 1249–1258 (2016)

43.

Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21–26, 2017, pp. 2261–2269. IEEE Computer Society (2017). https://doi.org/10.1109/CVPR.2017.243

44.

Zhou, Y., Shao, L.: Vehicle re-identification by adversarial bi-directional LSTM network. In: 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, Lake Tahoe, NV, USA, March 12–15, 2018, pp. 653–662. IEEE Computer Society (2018). https://doi.org/10.1109/WACV.2018.00077

45.

Zhu, J., Du, Y., Hu, Y., Zheng, L., Cai, C.: Vrsdnet: vehicle re-identification with a shortly and densely connected convolutional neural network. Multimed. Tools Appl. 78(20), 29043–29057 (2019). https://doi.org/10.1007/s11042-018-6270-4CrossRef

46.

Lou, Y., Bai, Y., Liu, J., Wang, S., Duan, L.: Embedding adversarial learning for vehicle re-identification. IEEE Trans. Image Process. 28(8), 3794–3807 (2019). https://doi.org/10.1109/TIP.2019.2902112MathSciNetCrossRefMATH

47.

Peng, J., Hao, Y., Xu, F., Fu, X.: Vehicle re-identification using multi-task deep learning network and spatio-temporal model. Multimed. Tools Appl. 79(43–44), 32731–32747 (2020). https://doi.org/10.1007/s11042-020-09356-wCrossRef

48.

Zheng, A., Lin, X., Dong, J., Wang, W., Tang, J., Luo, B.: Multi-scale attention vehicle re-identification. Neural Comput. Appl. 32(23), 17489–17503 (2020). https://doi.org/10.1007/s00521-020-05108-xCrossRef

49.

Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C., Zheng, L.: Vehicle re-identification using quadruple directional deep learning features. IEEE Trans. Intell. Transp. Syst. 21(1), 410–420 (2020). https://doi.org/10.1109/TITS.2019.2901312CrossRef

50.

Yuefeng, W., Ying, W., Ruipeng, M., Lin, W.: Unsupervised vehicle re-identification based on mixed sample contrastive learning. Signal Image Video Process. 16(8), 2083–2091 (2022). https://doi.org/10.1007/s11760-022-02170-xCrossRef

51.

Zhang, F., Ma, Y., Yuan, G., Zhang, H., Ren, J.: Multiview image generation for vehicle reidentification. Appl. Intell. 51(8), 5665–5682 (2021). https://doi.org/10.1007/s10489-020-02171-8CrossRef

Title: View-aware attribute-guided network for vehicle re-identification
Authors: Saifullah Tumrani
Wazir Ali
Rajesh Kumar
Abdullah Aman Khan
Fayaz Ali Dharejo
Publication date: 29-03-2023
Publisher: Springer Berlin Heidelberg
Published in: Multimedia Systems / Issue 4/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-023-01077-y

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