Long-duration tracking of individuals across large sites is a challenge. Trucks of individuals from disjoint fields of view need to be linked, despite the same person appearing in a different pose, from a different viewpoint, and under different illumination conditions. This is an identity-matching problem, which could be approached by using traditional biometric cues, such as face. However, practical scenarios prevent from relying on good quality acquisition of face images at standoff distance. In luck of stable biometric data, one can revert to the whole-body appearance information, provided that a person will not change clothes between sightings (Doretto et al., 2011) (Wang et al., 2007). This person re-identification problem can be approached by designing methods for learning whole-body appearance representations that are invariant to the high intra-class variance (Sabri et al., 2022) (Siyahjani et al., 2015) induced by the unrestricted nuisance factors of variations, i.e., pose, illumination, viewpoint, background, and sensor noise.


  1. ISVC
    Joint Discriminative and Metric Embedding Learning for Person Re-Identification Sabri, S. I., Randhawa, Z. A., and Doretto, G. In Advances in Visual Computing, 2022. Oral abstract bibTeX arXiv pdf doi
  2. ICCV
    A Supervised Low-rank Method for Learning Invariant Subspaces Siyahjani, F., Almohsen, R., Sabri, S., and Doretto, G. In Proceedings of IEEE International Conference on Computer Vision, 2015. abstract bibTeX pdf
  3. JAIHC
    Appearance-based person reidentification in camera networks: problem overview and current approaches Doretto, G., Sebastian, T., Tu, P., and Rittscher, J. Journal of Ambient Intelligence and Humanized Computing, 2011. abstract bibTeX pdf html
  4. ICCV
    Shape and appearance context modeling Wang, X., Doretto, G., Sebastian, T. B., Rittscher, J., and Tu, P. H. In Proceedings of IEEE International Conference on Computer Vision, 2007. abstract bibTeX pdf