Correlation Based Identity Filter: An Efficient Framework for Person Search

  • Wei-Hong Li
  • Yafang Mao
  • Ancong Wu
  • Wei-Shi ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10666)


Person search, which addresses the problem of re-identifying a specific query person in whole candidate images without bounding boxes in real-world scenarios, is a new topic in computer vision with many meaningful applications and has attracted much attention. However, it is inherently challenging because the annotations of pedestrian bounding boxes are unavailable and we have to identify find the target person from the whole gallery images. The existence of many visually similar people and dramatic appearance changes of the same person arising from the great cross-camera variation such as illumination, viewpoint, occlusions and background clutter also leads to the failure of searching a query person. In this work, we designed a Correlation based Identity Filter (CIF) framework for re-identifying the query person directly from the whole image with high efficiency. A regression model is learnt for obtaining a correlation filter/template for a given query person, which can help to alleviate the accumulated error caused by doing detection and re-identification separately. The filter is light and can be obtained and applied to search the query person with high speed with the utilization of Block-Circulant Decomposition (BCD) and Discrete Fourier Transform (DFT) techniques. Extensive experiments illustrate that our method has the important practical benefit of searching a specific person with a light weight and high efficiency and achieves better accuracy than doing detection and re-identification separately.


Person search Correlation based Identity Filter Regression Pedestrian detection Person re-identification 


  1. 1.
    Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: CVPR (2015)Google Scholar
  2. 2.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. TPAMI 34(4), 743–761 (2012)CrossRefGoogle Scholar
  3. 3.
    Henriques, J.F., Carreira, J., Caseiro, R., Batista, J.: Beyond hard negative mining: efficient detector learning via block-circulant decomposition. In: ICCV (2013)Google Scholar
  4. 4.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. TPAMI 37(3), 583–596 (2015)CrossRefGoogle Scholar
  5. 5.
    Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR (2012)Google Scholar
  6. 6.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR (2012)Google Scholar
  7. 7.
    Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: CVPR (2014)Google Scholar
  8. 8.
    Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: CVPR (2013)Google Scholar
  9. 9.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR (2015)Google Scholar
  10. 10.
    Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: ICCV (2015)Google Scholar
  11. 11.
    Lisanti, G., Masi, I., Del Bimbo, A.: Matching people across camera views using kernel canonical correlation analysis. In: ICDSC (2014)Google Scholar
  12. 12.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). CrossRefGoogle Scholar
  13. 13.
    Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(6), 379–390 (2014)CrossRefGoogle Scholar
  14. 14.
    Valmadre, J., Bertinetto, L., Henriques, J.F., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: CVPR (2017)Google Scholar
  15. 15.
    Xiao, J., Xie, Y., Tillo, T., Huang, K., Wei, Y., Feng, J.: IAN: the individual aggregation network for person search. arXiv preprint arXiv:1705.05552 (2017)
  16. 16.
    Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: CVPR (2016)Google Scholar
  17. 17.
    Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. arXiv preprint arXiv:1604.01850 (2017)
  18. 18.
    Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.Z.: Salient color names for person re-identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 536–551. Springer, Cham (2014). Google Scholar
  19. 19.
    Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wei-Hong Li
    • 1
  • Yafang Mao
    • 2
  • Ancong Wu
    • 1
  • Wei-Shi Zheng
    • 2
    Email author
  1. 1.School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

Personalised recommendations