SDALF for person re-identification

Symmetry-driven accumulation of local features for human characterization and re-identification
L. Bazzani, M. Cristani, V. Murino
Computer Vision and Image Understanding (CVIU), 2013.
SDALF code / bibtex
@article{Bazzani:CVIU13,
  title = {Symmetry-driven accumulation of local features
           for human characterization and re-identification},
  author = {Bazzani, Loris and Cristani, Marco and Murino, Vittorio},
  journal = {Comput. Vis. Image Underst.},
  year = {2013},
  month = feb,
  number = {2},
  pages = {130--144},
  volume = {117},
  doi = {10.1016/j.cviu.2012.10.008},
  issn = {1077-3142},
  issue_date = {February, 2013},
  numpages = {15},
  owner = {lbazzani},
  publisher = {Elsevier Science Inc.}
}
    

Person re-identification by symmetry-driven accumulation of local features
M. Farenzena, L. Bazzani, A. Perina, M. Cristani, V. Murino
In Conference on Computer Vision and Pattern Recognition (CVPR), 2010
SDALF code / video / bibtex
@inproceedings{Farenzena:CVPR10,
  title = {Person re-identification by symmetry-driven
         accumulation of local features},
  author = {Farenzena, M. and Bazzani, L. and Perina, A. and
         Murino, V. and Cristani, M.},
  booktitle = {IEEE Conference on Computer Vision and Pattern
        Recognition (CVPR)},
  year = {2010},
  month = {June},
  pages = {2360 -2367},
  bdsk-url-1 = {http://dx.doi.org/10.1109/CVPR.2010.5539926},
  doi = {10.1109/CVPR.2010.5539926},
  issn = {1063-6919}
}
    
Cite one of these papers if you use SDALF

Details

In this papers, we proposed an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual.

SDALF has been extensively tested on the following datasets:

Download SDALF

(link to github)

See the instructions in the README.md file.

Some cool videos



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