skip to main content
research-article

Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification

Authors Info & Claims
Published:13 January 2022Publication History
Skip Abstract Section

Abstract

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.

REFERENCES

  1. [1] Güler Rıza Alp, Neverova Natalia, and Kokkinos Iasonas. 2018. DensePose: Dense human pose estimation in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA, 72977306.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Andriluka Mykhaylo, Pishchulin Leonid, Gehler Peter, and Schiele Bernt. 2014. 2D human pose estimation: New benchmark and state of the art analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 36863693. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Chen Binghui, Deng Weihong, and Hu Jiani. 2019. Mixed high-order attention network for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’19). 371381.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Chen Tianlong, Ding Shaojin, Xie Jingyi, Yuan Ye, Chen Wuyang, Yang Yang, Ren Zhou, and Wang Zhangyang. 2019. ABD-Net: Attentive but diverse person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 83518361.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Chen Yanbei, Zhu Xiatian, and Gong Shaogang. 2017. Person re-identification by deep learning multi-scale representations. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 25902600.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Dai Zuozhuo, Chen Mingqiang, Gu Xiaodong, Zhu Siyu, and Tan Ping. 2018. Batch DropBlock network for person re-identification and beyond. arXiv:1811.07130.Google ScholarGoogle Scholar
  7. [7] Deng Jia, Dong Wei, Socher Richard, Li Li-Jia, Li Kai, and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE, Los Alamitos, CA, 248255.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Farenzena Michela, Bazzani Loris, Perina Alessandro, Murino Vittorio, and Cristani Marco. 2010. Person re-identification by symmetry-driven accumulation of local features. In Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’10). IEEE, Los Alamitos, CA, 23602367.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Felzenszwalb Pedro F., Girshick Ross B., McAllester David, and Ramanan Deva. 2009. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 9 (2009), 16271645. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Gheissari Niloofar, Sebastian Thomas B., and Hartley Richard. 2006. Person reidentification using spatiotemporal appearance. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. IEEE, Los Alamitos, CA, 15281535. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] Goyal Priya, Dollár Piotr, Girshick Ross, Noordhuis Pieter, Wesolowski Lukasz, Kyrola Aapo, Tulloch Andrew, Jia Yangqing, and He Kaiming. 2017. Accurate, large minibatch SGD: Training ImageNet in 1 hour. arXiv:1706.02677.Google ScholarGoogle Scholar
  12. [12] Guo Jianyuan, Yuan Yuhui, Huang Lang, Zhang Chao, Yao Jin-Ge, and Han Kai. 2019. Beyond human parts: Dual part-aligned representations for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’19). 36423651.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] He Kaiming, Gkioxari Georgia, Dollár Piotr, and Girshick Ross. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 29612969.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] He Kaiming, Zhang Xiangyu, Ren Shaoqing, and Sun Jian. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 770778.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Hermans Alexander, Beyer Lucas, and Leibe Bastian. 2017. In defense of the triplet loss for person re-identification. arXiv:1703.07737.Google ScholarGoogle Scholar
  16. [16] Hinton Geoffrey, Vinyals Oriol, and Dean Jeff. 2015. Distilling the knowledge in a neural network. arXiv:1503.02531.Google ScholarGoogle Scholar
  17. [17] Huang Yan, Sheng Hao, Zheng Yanwei, and Xiong Zhang. 2017. DeepDiff: Learning deep difference features on human body parts for person re-identification. Neurocomputing 241 (2017), 191203.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Huang Yan, Wu Qiang, Xu JingSong, and Zhong Yi. 2019. SBSGAN: Suppression of inter-domain background shift for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’19). 95279536.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Huang Yan, Xu Jingsong, Wu Qiang, Zheng Zhedong, Zhang Zhaoxiang, and Zhang Jian. 2019. Multi-pseudo regularized label for generated data in person re-identification. IEEE Transactions on Image Processing 28, 3 (2019), 13911403.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Huang Yan, Xu Jingsong, Wu Qiang, Zhong Yi, and Zhang Zhaoxiang. 2020. Beyond scalar neuron: Adopting vector-neuron capsules for long-term person re-identification. IEEE Transactions on Circuits and Systems for Video Technology 30, 10 (2020), 3459–3471.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Jaderberg Max, Simonyan Karen, Zisserman Andrew, and Koray Kavukcuoglu. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 20172025. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Kalayeh Mahdi M., Basaran Emrah, Gökmen Muhittin, Kamasak Mustafa E., and Shah Mubarak. 2018. Human semantic parsing for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 10621071.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Kingma Diederik P. and Ba Jimmy. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980.Google ScholarGoogle Scholar
  24. [24] Le Cuong Vo, Hong Quan Nguyen, Quang Trung Tran, and Trung Nghia Doan. 2016. Superpixel-based background removal for accuracy salience person re-identification. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia’16). IEEE, Los Alamitos, CA, 14.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Li Dangwei, Chen Xiaotang, Zhang Zhang, and Huang Kaiqi. 2017. Learning deep context-aware features over body and latent parts for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 384393.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Li Wei, Zhao Rui, Xiao Tong, and Wang Xiaogang. 2014. DeepReid: Deep filter pairing neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 152159. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Li Wei, Zhu Xiatian, and Gong Shaogang. 2017. Person re-identification by deep joint learning of multi-loss classification. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). 21942200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Li Wei, Zhu Xiatian, and Gong Shaogang. 2018. Harmonious attention network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 22852294.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Liang Xiaodan, Gong Ke, Shen Xiaohui, and Lin Liang. 2018. Look into person: Joint body parsing and pose estimation network and a new benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 4 (2018), 871885. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Lin Tsung-Yi, Maire Michael, Belongie Serge, Hays James, Perona Pietro, Ramanan Deva, Dollár Piotr, and Zitnick C. Lawrence. 2014. Microsoft COCO: Common objects in context. In Proceedings of the European Conference on Computer Vision (ECCV’14). 740755.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Liu Hao, Feng Jiashi, Qi Meibin, Jiang Jianguo, and Yan Shuicheng. 2017. End-to-end comparative attention networks for person re-identification. IEEE Transactions on Image Processing 26, 7 (2017), 34923506.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Liu Yiheng, Zhou Wengang, Liu Jianzhuang, Qi Guojun, Tian Qi, and Li Houqiang. 2019. An end-to-end foreground-aware network for person re-identification. arXiv:1910.11547.Google ScholarGoogle Scholar
  33. [33] Nguyen Thuy Binh, Pham Van Phu, Le Thi-Lan, and Le Cuong Vo. 2016. Background removal for improving saliency-based person re-identification. In Proceedings of the 2016 8th International Conference on Knowledge and Systems Engineering (KSE’16). IEEE, Los Alamitos, CA, 339344.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Paszke Adam, Gross Sam, Chintala Soumith, Chanan Gregory, Yang Edward, DeVito Zachary, Lin Zeming, Desmaison Alban, Antiga Luca, and Lerer Adam. 2017. Automatic differentiation in PyTorch. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS’17). 1–4.Google ScholarGoogle Scholar
  35. [35] Qi Lei, Huo Jing, Wang Lei, Shi Yinghuan, and Gao Yang. 2018. MaskReid: A mask based deep ranking neural network for person re-identification. arXiv:1804.03864.Google ScholarGoogle Scholar
  36. [36] Qian Xuelin, Fu Yanwei, Xiang Tao, Wang Wenxuan, Qiu Jie, Wu Yang, Jiang Yu-Gang, and Xue Xiangyang. 2018. Pose-normalized image generation for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV’18). 650667.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Ren Pengyuan and Li Jianmin. 2018. Factorized distillation: Training holistic person re-identification model by distilling an ensemble of partial Reid models. arXiv:1811.08073.Google ScholarGoogle Scholar
  38. [38] Ristani Ergys, Solera Francesco, Zou Roger, Cucchiara Rita, and Tomasi Carlo. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In Proceedings of the European Conference on Computer Vision (ECCV’16). 1735.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Sarfraz M. Saquib, Schumann Arne, Eberle Andreas, and Stiefelhagen Rainer. 2018. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 420429.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Selvaraju Ramprasaath R., Cogswell Michael, Das Abhishek, Vedantam Ramakrishna, Parikh Devi, and Batra Dhruv. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 618626.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Shen Yantao, Li Hongsheng, Xiao Tong, Yi Shuai, Chen Dapeng, and Wang Xiaogang. 2018. Deep group-shuffling random walk for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 22652274.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Song Chunfeng, Huang Yan, Ouyang Wanli, and Wang Liang. 2018. Mask-guided contrastive attention model for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 11791188.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Su Chi, Li Jianing, Zhang Shiliang, Xing Junliang, Gao Wen, and Tian Qi. 2017. Pose-driven deep convolutional model for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 39603969.Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Suh Yumin, Wang Jingdong, Tang Siyu, Mei Tao, and Lee Kyoung Mu. 2018. Part-aligned bilinear representations for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV’18). 402419.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Sun Yifan, Zheng Liang, Deng Weijian, and Wang Shengjin. 2017. SVDNet for pedestrian retrieval. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 38003808.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Sun Yifan, Zheng Liang, Yang Yi, Tian Qi, and Wang Shengjin. 2018. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European Conference on Computer Vision (ECCV’18). 480496.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Szegedy Christian, Vanhoucke Vincent, Ioffe Sergey, Shlens Jon, and Wojna Zbigniew. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 28182826.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Tian Maoqing, Yi Shuai, Li Hongsheng, Li Shihua, Zhang Xuesen, Shi Jianping, Yan Junjie, and Wang Xiaogang. 2018. Eliminating background-bias for robust person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 57945803.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Wang Cheng, Zhang Qian, Huang Chang, Liu Wenyu, and Wang Xinggang. 2018. Mancs: A multi-task attentional network with curriculum sampling for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV’18). 365381.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Wang Guanshuo, Yuan Yufeng, Chen Xiong, Li Jiwei, and Zhou Xi. 2018. Learning discriminative features with multiple granularities for person re-identification. In Proceedings of the 2018 ACM Multimedia Conference. ACM, New York, NY, 274282. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Wang Liang, Tan Tieniu, Ning Huazhong, and Hu Weiming. 2003. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 12 (2003), 15051518. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Wei Longhui, Zhang Shiliang, Yao Hantao, Gao Wen, and Tian Qi. 2017. Glad: Global-local-alignment descriptor for pedestrian retrieval. In Proceedings of the 25th ACM International Conference on Multimedia (MM’17). ACM, New York, NY, 420428. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Wu Junyi, Yao Lingxiang, Huang Yan, Xu Jingsong, Qiang Wu, and Huang Liqin. 2019. Improving person re-identification performance using body mask via cross-learning strategy. In Proceedings of the 2019 IEEE Visual Communications and Image Processing (VCIP’19). IEEE, Los Alamitos, CA, 14.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Xu Jing, Zhao Rui, Zhu Feng, Wang Huaming, and Ouyang Wanli. 2018. Attention-aware compositional network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 21192128.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Yu Rui, Zhou Zhichao, Bai Song, and Bai Xiang. 2017. Divide and fuse: A re-ranking approach for person re-identification. arXiv:1708.04169.Google ScholarGoogle Scholar
  56. [56] Zhang Zhizheng, Lan Cuiling, Zeng Wenjun, and Chen Zhibo. 2019. Densely semantically aligned person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 667676.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Zhao Haiyu, Tian Maoqing, Sun Shuyang, Shao Jing, Yan Junjie, Yi Shuai, Wang Xiaogang, and Tang Xiaoou. 2017. 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 (CVPR’17). 10771085.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Zheng Liang, Huang Yujia, Lu Huchuan, and Yang Yi. 2019. Pose invariant embedding for deep person re-identification. IEEE Transactions on Image Processing 28, 9 (2019), 4500–4509.Google ScholarGoogle Scholar
  59. [59] Zheng Liang, Shen Liyue, Tian Lu, Wang Shengjin, Wang Jingdong, and Tian Qi. 2015. Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 11161124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] Zheng Liang, Yang Yi, and Hauptmann Alexander G.. 2016. Person re-identification: Past, present and future. arXiv:1610.02984.Google ScholarGoogle Scholar
  61. [61] Zheng Meng, Karanam Srikrishna, Wu Ziyan, and Radke Richard J.. 2019. Re-identification with consistent attentive siamese networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). 57355744.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. [62] Zheng Zhedong, Yang Xiaodong, Yu Zhiding, Zheng Liang, Yang Yi, and Kautz Jan. 2019. Joint discriminative and generative learning for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19).Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Zheng Zhedong, Zheng Liang, and Yang Yi. 2018. A discriminatively learned CNN embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications 14, 1 (2018), 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. [64] Zhong Zhun, Zheng Liang, Cao Donglin, and Li Shaozi. 2017. Re-ranking person re-identification with k-reciprocal encoding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 13181327.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Zhou Kaiyang, Yang Yongxin, Cavallaro Andrea, and Xiang Tao. 2019. Omni-scale feature learning for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’19).Google ScholarGoogle ScholarCross RefCross Ref
  66. [66] Zhou Sanping, Wang Jinjun, Meng Deyu, Liang Yudong, Gong Yihong, and Zheng Nanning. 2019. Discriminative feature learning with foreground attention for person re-identification. arXiv:1807.01455.Google ScholarGoogle Scholar
  67. [67] Zhu Kuan, Guo Haiyun, Liu Zhiwei, Tang Ming, and Wang Jinqiao. 2020. Identity-guided human semantic parsing for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV’20).Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 4
      November 2021
      529 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3492437
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 January 2022
      • Revised: 1 January 2021
      • Accepted: 1 January 2021
      • Received: 1 July 2020
      Published in tomm Volume 17, Issue 4

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!