Advertisement

Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method

  • Zhongyi Li
  • Yi JinEmail author
  • Yidong Li
  • Congyan Lang
  • Songhe Feng
  • Tao Wang
Article
  • 24 Downloads
Part of the following topical collections:
  1. Computational Social Science as the Ultimate Web Intelligence

Abstract

Person re-identification is to identify a target person in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of persons, diversified illuminations, and complicated environments. In addition, body parts are usually misaligned because of the less precise bounding boxes, which play a significant role in person re-identification, so it is crucial to make them aligned for better performance. In this paper, we propose a network to learn powerful features combining global features and local-alignment features for person re-identification. For each body part, instead of fixed horizontal partition, a key points detection network is adopted to locate body parts that contain more precise and distinctive information. Besides, a novel re-ranking approach is proposed to refine the rough initial rank list by exploiting the spatial-temporal information. Unlike most existing re-ranking based methods fine-tuning the rough initial rank list only by k-nearest neighbors and their k-reverse-nearest neighbors, our method exploits spatial-temporal information which can be easily stored in the name of images, so it can be implemented in any baseline to improve the performance. Experiments on the GRID, Market-1501, and DukeMTMC-reID are conducted to prove the effectiveness of our method.

Keywords

Person re-identification Part alignment Re-ranking 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.61972030), the Fundamental Research Funds for Central Universities (No. 2018JBM017) and the Hebei Province Key Research and Development Projects(18210305D).

References

  1. 1.
    Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3908–3916 (2015)Google Scholar
  2. 2.
    Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. Computer Vision & Pattern Recognition (2015)Google Scholar
  3. 3.
    Almazan, J., Gajic, B., Murray, N., Larlus, D.: Re-id done right: Towards good practices for person re-identification. arXiv:1801.05339 (2018)
  4. 4.
    Chen, D., Yuan, Z., Chen, B., Zheng, N.: Similarity learning with spatial constraints for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1268–1277 (2016)Google Scholar
  5. 5.
    Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: A deep quadruplet network for person re-identification. In: IEEE Conference on Computer Vision & Pattern Recognition (2017)Google Scholar
  6. 6.
    Chen, W., Chen, X., Zhang, J., Huang, K.: A multi-task deep network for person re-identification. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  7. 7.
    De, C., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1335–1344 (2016)Google Scholar
  8. 8.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  9. 9.
    Garcia, J., Martinel, N., Micheloni, C., Gardel, A.: Person re-identification ranking optimisation by discriminant context information analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1305–1313 (2015)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778 (2016)Google Scholar
  11. 11.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification (2017)Google Scholar
  12. 12.
    Kalayeh, M.M., Basaran, E., Gokmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification (2018)Google Scholar
  13. 13.
    Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2288–2295. IEEE (2012)Google Scholar
  14. 14.
    Leng, Q., Hu, R., Liang, C., Wang, Y., Chen, J.: Bidirectional ranking for person re-identification. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp 1–6. IEEE (2013)Google Scholar
  15. 15.
    Li, W., Rui, Z., Tong, X., Wang, X.G.: Deepreid: Deep filter pairing neural network for person re-identification. Computer Vision & Pattern Recognition (2014)Google Scholar
  16. 16.
    Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: Deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 152–159 (2014)Google Scholar
  17. 17.
    Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification (2017)Google Scholar
  18. 18.
    Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. arXiv:1705.04724 (2017)
  19. 19.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: 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 (2015)Google Scholar
  20. 20.
    Liao, S., Li, S.Z.: Efficient psd constrained asymmetric metric learning for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3685–3693 (2015)Google Scholar
  21. 21.
    Liu, C., Loy, C.C., Gong, S., Wang, G.: Pop: Person re-identification post-rank optimisation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 441–448 (2013)Google Scholar
  22. 22.
    Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis (2009)Google Scholar
  23. 23.
    Lv, J., Chen, W., Li, Q., Yang, C.: Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7948–7956 (2018)Google Scholar
  24. 24.
    Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical gaussian descriptor for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1363–1372 (2016)Google Scholar
  25. 25.
    Peng, P., Xiang, T., Wang, Y., Pontil, M., Tian, Y.: Unsupervised cross-dataset transfer learning for person re-identification. Computer Vision & Pattern Recognition (2016)Google Scholar
  26. 26.
    Rui, Z., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. Computer Vision & Pattern Recognition (2013)Google Scholar
  27. 27.
    Rui, Z., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. Computer Vision & Pattern Recognition (2014)Google Scholar
  28. 28.
    Shi, H., Yang, Y., Zhu, X., Liao, S., Zhen, L., Zheng, W., Li, S.Z.: Embedding deep metric for person re-identification: A study against large variations (2016)CrossRefGoogle Scholar
  29. 29.
    Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1179–1188 (2018)Google Scholar
  30. 30.
    Su, C., Zhang, S., Xing, J., Wen, G., Qi, T.: Deep attributes driven multi-camera person re-identification. In: European Conference on Computer Vision (2016)Google Scholar
  31. 31.
    Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Qi, T.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3960–3969 (2017)Google Scholar
  32. 32.
    Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K.M.: Part-aligned bilinear representations for person re-identification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 402–419 (2018)CrossRefGoogle Scholar
  33. 33.
    Sun, Y., Zheng, L., Yi, Y., Qi, T., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European Conference on Computer Vision (ECCV), pp 480–496 (2018)CrossRefGoogle Scholar
  34. 34.
    Tong, X., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. Computer Vision & Pattern Recognition (2016)Google Scholar
  35. 35.
    Varior, R., Shuai, B., Lu, J., Xu, D., Wang, G.: A siamese long short-term memory architecture for human re-identification. In: European Conference on Computer Vision, pp 135–153. Springer (2016)Google Scholar
  36. 36.
    Varior, R.R., Haloi, M., Gang, W.: Gated siamese convolutional neural network architecture for human re-identification. In: European Conference on Computer Vision (2016)Google Scholar
  37. 37.
    Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K.: Region-based interactive ranking optimization for person re-identification. In: Pacific Rim Conference on Multimedia, pp 1–10. Springer (2014)Google Scholar
  38. 38.
    Wang, F., Zuo, W., Liang, L., Zhang, D., Lei, Z.: Joint learning of single-image and cross-image representations for person re-identification. Computer Vision & Pattern Recognition (2016)Google Scholar
  39. 39.
    Wang, H., Gong, S., Zhu, X., Xiang, T.: Human-in-the-loop person re-identification. In: European Conference on Computer Vision, pp 405–422. Springer (2016)Google Scholar
  40. 40.
    Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. arXiv:1804.01438 (2018)
  41. 41.
    Wei, L., Zhang, S., Yao, H., Wen, G., Qi, T., Wei, L., Zhang, S., Yao, H., Wen, G., Qi, T.: Glad: Global-local-alignment descriptor for pedestrian retrieval (2017)Google Scholar
  42. 42.
    Wei, L., Zhu, X, Gong, S.: Harmonious attention network for person re-identification (2018)Google Scholar
  43. 43.
    Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 466–481 (2018)CrossRefGoogle Scholar
  44. 44.
    Xu, J., Rui, Z., Feng, Z., Wang, H., Ouyang, W.: Attention-aware compositional network for person re-identification (2018)Google Scholar
  45. 45.
    Xu, J., Zhao, R., Zhu, F., Wang, H., Ouyang, W.: Attention-aware compositional network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2119–2128 (2018)Google Scholar
  46. 46.
    Ye, M., Chen, J., Leng, Q., Liang, C., Wang, Z., Sun, K.: Coupled-view based ranking optimization for person re-identification. In: International Conference on Multimedia Modeling, pp 105–117. Springer (2015)Google Scholar
  47. 47.
    Ye, M., Liang, C., Yu, Y., Wang, Z., Leng, Q., Xiao, C., Chen, J., Hu, R.: Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Trans. Multimed. 18(12), 2553–2566 (2016)CrossRefGoogle Scholar
  48. 48.
    Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2528–2535 (2013)Google Scholar
  49. 49.
    Zhao, H., Tian, M., Sun, S., Shao, J., Yan, J., Yi, S., Wang, X., Tang, X.: Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1077–1085 (2017)Google Scholar
  50. 50.
    Zheng, L., Shen, L., Lu, T., Wang, S., Wang, J., Qi, T.: Scalable person re-identification: A benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1116–1124 (2015)Google Scholar
  51. 51.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv:1610.02984 (2016)
  52. 52.
    Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q., et al.: Person re-identification in the wild. CVPR 1, 2 (2017)Google Scholar
  53. 53.
    Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification. arXiv:1701.07732 (2017)
  54. 54.
    Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. arXiv:1701.07717. 3 (2017)
  55. 55.
    Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3652–3661. IEEE (2017)Google Scholar
  56. 56.
    Zhou, J., Yu, P., Tang, W., Wu, Y.: Efficient online local metric adaptation via negative samples for person reidentification. In: The IEEE International Conference on Computer Vision (ICCV), vol. 2, p 7 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBeijing Jiaotong UniversityBeijingPeople’s Republic of China

Personalised recommendations