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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)

Abstract

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.

Keywords

Person Re-identification PRID Re-ID 

References

  1. 1.
    Alkoot, F.M., Kittler, J.: Experimental evaluation of expert fusion strategies. Pattern Recogn. Lett. 20(11–13), 1361–1369 (1999).  https://doi.org/10.1016/S0167-8655(99)00107-5CrossRefGoogle 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).  https://doi.org/10.1016/j.imavis.2014.02.001CrossRefGoogle 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).  https://doi.org/10.5194/isprs-archives-XLII-2-W8-37-2017CrossRefGoogle 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).  https://doi.org/10.1007/978-3-030-11009-3_10CrossRefGoogle 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).  https://doi.org/10.1109/CVPR.2017.145
  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).  https://doi.org/10.1109/CVPR.2009.5206848
  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).  https://doi.org/10.1109/CVPR.2016.138
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE (2016).  https://doi.org/10.1109/CVPR.2016.90
  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).  https://doi.org/10.1109/JSEN.2016.2579645CrossRefGoogle 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).  https://doi.org/10.1007/11881599_54CrossRefGoogle 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).  https://doi.org/10.1109/34.667881CrossRefGoogle 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).  https://doi.org/10.1109/CVPR.2012.6247939
  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).  https://doi.org/10.1109/CVPR.2014.27
  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).  https://doi.org/10.24963/ijcai.2017/305
  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).  https://doi.org/10.1109/CVPR.2015.7298832
  21. 21.
    Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: CVPR, pp. 1363–1372. IEEE (2016).  https://doi.org/10.1109/CVPR.2016.152
  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).  https://doi.org/10.1109/CVPR.2016.148
  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).  https://doi.org/10.1007/978-3-319-46478-7_9CrossRefGoogle 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).  https://doi.org/10.1145/2543581.2543596CrossRefGoogle 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).  https://doi.org/10.1109/TIP.2017.2675201MathSciNetCrossRefGoogle 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).  https://doi.org/10.1007/978-3-319-46466-4_42CrossRefGoogle 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).  https://doi.org/10.1109/ICCV.2017.113
  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).  https://doi.org/10.1109/CVPR.2017.103
  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).  https://doi.org/10.1109/ICCV.2015.133
  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).  https://doi.org/10.1109/CVPR.2015.7298783
  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).  https://doi.org/10.1109/CVPR.2017.389
  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).  https://doi.org/10.1109/CVPR.2017.717

Copyright information

© 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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