Advertisement

Spatial Invariant Person Search Network

  • Liangqi Li
  • Hua YangEmail author
  • Lin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

A cascaded framework is proposed to jointly integrate the associated pedestrian detection and person re-identification in this work. The first part of the framework is a Pre-extracting Net which acts as a feature extractor to produce low-level feature maps. Then a PST (Pedestrian Space Transformer), including a Pedestrian Proposal Net to generate person candidate bounding boxes, is introduced as the second part with affine transformation and down-sampling models to help avoid the spatial variance challenges related to resolutions, viewpoints and occlusions of person re-identification. After further extracting by a convolutional net and a fully connected layer, the resulting features can be used to produce outputs for both detection and re-identification. Meanwhile, we design a directionally constrained loss function to supervise the training process. Experiments on the CUHK-SYSU dataset and the PRW dataset show that our method remarkably enhances the performance of person search.

Keywords

Person re-identification Person search Spatial transformation 

Notes

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (NSFC, Grant No. 61771303 and 61671289), Science and Technology Com- mission of Shanghai Municipality (STCSM, Grant Nos. 17DZ1205602,18DZ1200102), and SJTU-Yitu/Thinkforce Joint laboratory for visual computing and application.

References

  1. 1.
    Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1259–1267, June 2016Google Scholar
  2. 2.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)CrossRefGoogle Scholar
  3. 3.
    Ess, A., Müller, T., Grabner, H., Van Gool, L.J.: Segmentation-based urban traffic scene understanding. In: BMVC, vol. 1, p. 2. Citeseer (2009)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)Google Scholar
  7. 7.
    Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3376–3385. IEEE (2017)Google Scholar
  8. 8.
    Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q., et al.: Person re-identification in the wild. In: CVPR, vol. 1, p. 2 (2017)Google Scholar
  9. 9.
    Liu, H., et al.: Neural person search machines. In: ICCV, pp. 493–501 (2017)Google Scholar
  10. 10.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, no. 2, p. 3 (2017)Google Scholar
  11. 11.
    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
  12. 12.
    Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel CNN with improved triplet loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)Google Scholar
  13. 13.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  14. 14.
    Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)Google Scholar
  15. 15.
    Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Paszke, A., et al.: Automatic differentiation in PyTorch (2017)Google Scholar
  17. 17.
    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
  18. 18.
    Yang, J., Wang, M., Li, M., Zhang, J.: Enhanced deep feature representation for person search. In: Yang, J. (ed.) CCCV 2017. CCIS, vol. 773, pp. 315–327. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-7305-2_28CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Shanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations