Person Re-identification Using Masked Keypoints

  • Diego Reyes
  • John AtkinsonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)


In this work, a method for person re-identification from surveillance videos is proposed. In this approach, person detection is based on moving objects from sequences of images, and on incorporating a feature extraction technique that can distinguish distinct persons according to their physical appearance by using masked images that reduce noise from the background. Our approach uses keypoints to build an image’s descriptor so that the best discriminative keypoints can be identified between different persons. Experiments using our masked re-identification method show significant improvements in the recognition rate when masked frames are used to reduce noise of the second plane.



This research was supported by FONDECYT (Chile) under grant number 1170002: “An effective Linguistically-motivated computational model for opinion retrieval in sentiment analysis tasks”.


  1. 1.
    Bauml, M., Stiefelhagen, R.: Evaluation of local features for person re-identification in image sequences. In: 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Klagenfurt, Austria (2011)Google Scholar
  2. 2.
    Bazzani, L., Cristani, M., Perina, A., Murino, V.: Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn. Lett. 33(7), 898–903 (2012). Special Issue on Awards from ICPR 2010CrossRefGoogle Scholar
  3. 3.
    Cheng, D.S., Cristani, M.: Person re-identification by articulated appearance matching. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 139–160. Springer, London (2014). Scholar
  4. 4.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Computer Vision and Pattern Recognition, San Francisco, CA (2010)Google Scholar
  5. 5.
    Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: Second ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (2008)Google Scholar
  6. 6.
    Karanam, S., Yang, L., Richard, J.: Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: IEEE International Conference on Computer Vision (2015)Google Scholar
  7. 7.
    Khedher, M., El-Yacoubi, M., Dorizzi, B.: Fusion of appearance and motion-based sparse representations for multi-shot person re-identification. Neurocomputing 248, 94–104 (2017)CrossRefGoogle Scholar
  8. 8.
    Kviatkovsky, I., Adam, A., Rivlin, E.: Color invariants for person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1622–1634 (2013)CrossRefGoogle Scholar
  9. 9.
    Lisanti, G., Masi, I., Bagdanov, A., Del Bimbo, A.: Person re-identication by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1629–1642 (2015)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Gong, S., Loy, C.C., Lin, X.: Person re-identification: what features are important? In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7583, pp. 391–401. Springer, Heidelberg (2012). Scholar
  11. 11.
    Mazzon, R., Tahir, S., Syed, F., Cavallaro, A.: Person re-identification in crowd. Pattern Recogn. Lett. 33(14), 1828–1837 (2012)CrossRefGoogle Scholar
  12. 12.
    Munaro, M., Ghidoni, S., Tartaro, D., Menegatti, E.: A feature-based approach to people re-identification using skeleton keypoints. In: IEEE International Conference on Robotics and Automation, Hong Kong, China (2014)Google Scholar
  13. 13.
    Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3318–3325, June 2013Google Scholar
  14. 14.
    Xiao, L., Mingli, S., Dacheng, T., Xingchen, Z., Chen, C., Jiajun, B.: Semi-supervised coupled dictionary learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3550–3557, June 2014Google Scholar
  15. 15.
    Zheng, W., Gong, S., Xiang, T.: Reidentification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad Adolfo IbañezSantiagoChile

Personalised recommendations