View-Aware Person Re-identification

  • Gregor BlottEmail author
  • Jie Yu
  • Christian Heipke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)


Appearance-based person re-identification (PRID) is currently an active and challenging research topic. Recently proposed approaches have mostly dealt with low- and middle-level processing of images. Furthermore, there is very limited research that has focused on view information. View variation limits the performance of most approaches because a person’s appearance from one view can be completely different from that of another view, which makes the re-identification challenging. In this work, we study the influence of the view on PRID and propose several fusion strategies that utilize multi-view information to handle the PRID problem. We perform experiments on a re-mapped version of Market-1501 dataset and an internal dataset. Our proposed multi-view strategy increases the recognition rate at rank-one by a large margin in comparison with that obtained via random view matching or multi-shot.


Person Re-identification PRID Re-ID 


  1. 1.
    Alkoot, F.M., Kittler, J.: Experimental evaluation of expert fusion strategies. Pattern Recogn. Lett. 20(11–13), 1361–1369 (1999). Scholar
  2. 2.
    Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270–286 (2014). Scholar
  3. 3.
    Blott, G., Heipke, C.: Bifocal stereo for multipath person re-identification. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W8, pp. 37–44 (2017). Scholar
  4. 4.
    Blott, G., Takami, M., Heipke, C.: Semantic segmentation of fisheye images. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018 Workshops. LNCS, vol. 11129, pp. 181–196. Springer, Cham (2019). Scholar
  5. 5.
    Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: CVPR, pp. 1320–1329. IEEE (2017).
  6. 6.
    Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE (2009).
  7. 7.
    Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person re-identification. CoRR abs/1611.05244 (2016)Google Scholar
  8. 8.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: International Workshop on Performance Evaluation for Tracking and Surveillance, Rio de Janeiro. IEEE (2007)Google Scholar
  9. 9.
    Haque, A., Alahi, A., Fei-Fei, L.: Recurrent attention models for depth-based person identification. In: CVPR, pp. 1229–1238. IEEE (2016).
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE (2016).
  11. 11.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. CoRR abs/1703.07737 (2017)Google Scholar
  12. 12.
    Imani, Z., Soltanizadeh, H.: Person reidentification using local pattern descriptors and anthropometric measures from videos of kinect sensor. IEEE Sens. J. 16(16), 6227–6238 (2016). Scholar
  13. 13.
    Jović, M., Hatakeyama, Y., Dong, F., Hirota, K.: Image retrieval based on similarity score fusion from feature similarity ranking lists. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 461–470. Springer, Heidelberg (2006). Scholar
  14. 14.
    Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O.I., Radke, R.J.: A comprehensive evaluation and benchmark for person re-identification: features, metrics, and datasets. CoRR abs/1605.09653 (2016)Google Scholar
  15. 15.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998). Scholar
  16. 16.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295. IEEE (2012).
  17. 17.
    Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: CVPR, pp. 152–159. IEEE (2014).
  18. 18.
    Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. In: Sierra, C. (ed.) IJCAI, pp. 2194–2200 (2017).
  19. 19.
    Li, X., et al.: Video object segmentation with re-identification. CoRR abs/1708.00197 (2017)Google Scholar
  20. 20.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, pp. 2197–2206. IEEE (2015).
  21. 21.
    Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: CVPR, pp. 1363–1372. IEEE (2016).
  22. 22.
    McLaughlin, N., del Rincón, J.M., Miller, P.C.: Recurrent convolutional network for video-based person re-identification. In: CVPR, pp. 1325–1334. IEEE (2016).
  23. 23.
    Riachy, C., Bouridane, A.: Person re-identification: attribute-based feature evaluation. In: 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 85–90. IEEE (2018)Google Scholar
  24. 24.
    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). Scholar
  25. 25.
    Vezzani, R., Baltieri, D., Cucchiara, R.: People reidentification in surveillance and forensics: a survey. ACM Comput. Surv. 46(2), 29–37 (2013). Scholar
  26. 26.
    Witten, I.H., Eibe, F., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Elsevier (2011)zbMATHGoogle Scholar
  27. 27.
    Wu, A., Zheng, W., Lai, J.: Robust depth-based person re-identification. IEEE Trans. Image Process. 26(6), 2588–2603 (2017). Scholar
  28. 28.
    Yan, Y., Ni, B., Song, Z., Ma, C., Yan, Y., Yang, X.: Person re-identification via recurrent feature aggregation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 701–716. Springer, Cham (2016). Scholar
  29. 29.
    Yu, H., Wu, A., Zheng, W.: Cross-view asymmetric metric learning for unsupervised person re-identification. In: ICCV, pp. 994–1002. IEEE (2017).
  30. 30.
    Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. CoRR abs/1706.00384 (2017)Google Scholar
  31. 31.
    Zhao, H., et al.: Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: CVPR, pp. 907–915. IEEE (2017).
  32. 32.
    Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV, pp. 1116–1124. IEEE (2015).
  33. 33.
    Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., Tian, Q.: Query-adaptive late fusion for image search and person re-identification. In: CVPR, pp. 1741–1750. IEEE (2015).
  34. 34.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. CoRR abs/1610.02984 (2016)Google Scholar
  35. 35.
    Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: CVPR, pp. 3652–3661. IEEE (2017).
  36. 36.
    Zhou, Z., Huang, Y., Wang, W., Wang, L., Tan, T.: See the forest for the trees: joint spatial and temporal recurrent neural networks for video-based person re-identification. In: CVPR, pp. 6776–6785. IEEE (2017).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Vision Research LabRobert Bosch GmbHHildesheimGermany
  2. 2.Institute of Photogrammetry and GeoInformationLeibniz Universität HannoverHannoverGermany

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