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Joint Pyramid Feature Representation Network for Vehicle Re-identification

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Abstract

Vehicle re-identification (Re-ID) technology plays an important role in the intelligent transportation system for smart city. Due to various uncertain factors in the real-world scenarios, (e.g., resolution variation, viewpoint variation, illumination changes, occlusion, etc., vehicle Re-ID is a very challenging task. To resist the adverse effect of resolution variation, a joint pyramid feature representation network (JPFRN) for vehicle Re-ID is proposed in this paper. Based on the consideration that various convolution blocks with different depths hold different resolutions and semantic information of the vehicle image, the proposed JPFRN method employs a base network to obtain multi-resolution vehicle features in the first stage. Then, a pyramid feature representation scheme is developed to reconstruct and integrate the obtained multi-resolution vehicle features together. Finally, these pyramid features are jointly represented for learning a more discriminative feature under the supervision of joint Triplet loss and softmax loss. Extensive experimental results on two commonly-used vehicle databases (i.e., VehicleID and VeRi) show that the proposed JPFRN is superior to multiple recently-developed vehicle Re-ID methods.

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References

  1. Kuutti S, Fallah S, Katsaros K, Dianati K, Mccullough F, Mouzakitis A (2018) A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet Things J 5(2):829–46

    Article  Google Scholar 

  2. Zhu J, Huang J, Zeng H, Ye X, Li B, Lei Z, Zheng L (2020) Object re-identification via joint quadruple decorrelation directional deep networks in smart transportation. IEEE Internet Things J 1-1

  3. García-Magariño I, Sendra S, Lacuesta R, Lloret J (2019) Security in vehicles with IoT by prioritization rules, vehicle certificates, and trust management. IEEE Internet Things J 6(4):2372–2541

    Article  Google Scholar 

  4. Yang L, Luo P, Change Loy C, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 3973–3981

  5. Sochor J, Herout A, Havel J (2016) BoxCars: 3D boxes as cnn input for improved fine-grained vehicle recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3006–3015

  6. Matei BC, Sawhney HS, Samarasekera S (2011) Vehicle tracking across nonoverlapping cameras using joint kinematic and appearance features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3465–3472

  7. Guo J, Hsia C, Wong K, Wu J, Wu Y, Wang N (2015) Nighttime vehicle lamp detection and tracking with adaptive mask training. IEEE Trans Veh Technol 65(6):4023–4032

    Article  Google Scholar 

  8. Chen X, Xiang S, Liu C, Pan C (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801

    Article  Google Scholar 

  9. Fan Q, Brown L, Smith J (2016) A closer look at Faster R-CNN for vehicle detection. In: Proceedings of the IIEEE intelligent vehicles symposium, pp 124–129

  10. Liu H, Tian Y, Yang Y, Pang L, Huang T (2016) 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

  11. Zhu J, Zeng H, Du Y, Lei Z, Zheng L, Cai C (2018) Joint feature and similarity deep learning for vehicle re-identification. IEEE Access 6:43724–43731

    Article  Google Scholar 

  12. Velera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. IEE Proc - Vision Image Signal Process 152(2):192–204

    Article  Google Scholar 

  13. Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol 5(3):38

    Google Scholar 

  14. Liu X, Liu W, Ma H, Fu H (2016) In: Proceedings of the IEEE international conference on multimedia and expo, Chongqing

  15. Lin W, Tong D (2011) Vehicle re-identification with dynamic time windows for vehicle passage time estimation. IEEE Trans Intell Trans Syst 12(4):1057–1063

    Article  Google Scholar 

  16. Kwong K, Kavaler R, Rajagopal R, Varaiya P (2009) Arterial travel time estimation based on vehicle re-identification using wireless sensors. Transp Res Part C: Emerg Technol 17(6):586–606

    Article  Google Scholar 

  17. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3586–3593

  18. Liao S, Hu Y, Zhu X, Li SZ (2015) 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

  19. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1116–1124

  20. Song C, Huang Y, Ouyang W, Wang L (2018) Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1179–1188

  21. Cai L, Zhu J, Zeng H, Chen J, Cai C, Ma KK (2018) HOG-assisted deep feature learning for pedestrian gender recognition. J Franklin Inst 355(4):1991–2008

    Article  Google Scholar 

  22. Zhu J, Zeng H, Huang J, Zhu X, Lei Z, Cai L, Zheng L (2019) Body symmetry and part locality guided direct nonparametric deep feature enhancement for person re-identification. IEEE Internet Things J 1-1

  23. Zhu J, Zeng H, Liao S, Lei Z, Cai C, Zheng L (2018) Deep hybrid similarity learning for person re-identification. IEEE Trans Circ Syst Video Technol 20(11):3183–3193

    Article  Google Scholar 

  24. Farenzena M, Bazzani L, Parina A, Murino V, Cristani M (2012) Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2360–2367

  25. Liu X, Liu W, Mei T, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Proceedings of the European conference on computer vision, pp 869–884

  26. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, pp 1097–1105

  27. Simonyan K, Zisserman A (2014) Very deep convolutional networks for largescale image recognition. arXiv preprint arXiv:1409.1556

  28. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  29. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  30. Bai Y, Lou Y, Gao F, Wang S, Wu Y, Duan L (2018) Group-sensitive triplet embedding for vehicle reidentification. IEEE Trans Multimed 20(9):2385–2399

    Article  Google Scholar 

  31. Zhang Y, Liu D, Zha Z (2017) Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: Proceedings of thee IEEE international conference on multimedia and expo, pp 1386–1391

  32. Zhou Y, Shao L (2018) Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6489–6498

  33. Zhou Y, Liu L, Shao L (2018) Vehicle re-identification by deep hidden multi-view inference. IEEE Trans Image Process 27(7):3275–3287

    Article  MathSciNet  Google Scholar 

  34. Hou J, Zeng H, Cai L, Zhu J, Chen J, Ma KK (2019) IMulti-label learning with multi-label smoothing regularization for vehicle re-identification. In: INeurocomputing, pp 15–22

  35. Zhu J, Zeng H, Huang J, Liao S, Lei Z, Cai C, Zheng L (2019) Vehicle re-identification using quadruple directional deep learning features. IEEE Trans Intell Transp Syst 21(1):410–420

    Article  Google Scholar 

  36. Ni Z, Zeng H, Ma L, Hou J, Chen J, Ma K (2018) A Gabor feature-based quality assessment model for the screen content images. IEEE Trans Image Process 27(9):4516–4528

    Article  MathSciNet  Google Scholar 

  37. Fu Y, Zeng H, Ma L, Ni Z, Zhu J, Ma K (2018) Screen content image quality assessment using multi-scale difference of Gaussian. IEEE Trans Circ Syst Video Technol 28(9):2428–2432

    Article  Google Scholar 

  38. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

  39. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  40. Hou J, Zeng H, Zhu J, Hou J, Chen J, Ma KK (2019) Deep quadruplet appearance learning for vehicle re-identification. In: IEEE Transactions on Vehicular Technology, pp 1–1

  41. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Proceedings of the advances in neural information processing systems, pp 1988–1996

  42. Zheng Z, Zheng L, Yang Y (2018) A discriminatively learned CNN embedding for person reidentification. ACM Trans Multimed Comput Commun Appl (TOMM) 14(1):13

    MathSciNet  Google Scholar 

  43. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE international workshop on performance evaluation for tracking and surveillance (PETS), vol 3(5), pp 1–7

  44. Liu X, Liu W, Mei T, Ma H (2018) PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20(3):645–658

    Article  Google Scholar 

  45. Li Y, Li Y, Yan H, Liu J (2017) Deep joint discriminative learning for vehicle re-identification and retrieval. In: Proceedings of the IEEE international conference on image processing, pp 395–399

  46. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 700–4708

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under the grants 61871434, 61602191, and 61802136, in part by the Natural Science Foundation for Outstanding Young Scholars of Fujian Province under the grant 2019J06017, in part by the Natural Science Foundation of Fujian Province under the grant 2017J05103, in part by the Fujian-100 Talented People Program, in part by the Key Science and Technology Project of Xiamen City under the grant 3502ZCQ20191005, in part by High-level Talent Innovation Program of Quanzhou City under the grant 2017G027, in part by the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under the grants ZQN-YX403 and ZQN-PY418, and in part by the High-Level Talent Project Foundation of Huaqiao University under the grants 14BS201, 14BS204 and 16BS108, and in part by the Subsidized Project for Postgraduates Innovative Fund in Scientific Research of Huaqiao University.

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Authors and Affiliations

  1. School of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China

    Xiangwei Lin, Huanqiang Zeng, Jinhui Hou & Jing Chen

  2. Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jiuwen Cao

  3. School of Engineering, Huaqiao University, Quanzhou, 362021, China

    Jianqing Zhu

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  1. Xiangwei Lin
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  2. Huanqiang Zeng
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  3. Jinhui Hou
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Correspondence to Huanqiang Zeng.

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Lin, X., Zeng, H., Hou, J. et al. Joint Pyramid Feature Representation Network for Vehicle Re-identification. Mobile Netw Appl 25, 1781–1792 (2020). https://doi.org/10.1007/s11036-020-01561-z

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