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Reid_AER_onVIPeR

INFORMATION:

This is the Matlab code of our work Zhaoju Li, Zhenjun Han, Qixiang Ye:"Person Re-identification via AdaBoost Ranking Ensemble". In ICIP 2016. It has been conducted on Windows System, Ubuntu System untested. Two demos are provided:

  • Ada_demo.m : single experiment for validation
  • Average_ada.m : ten trials of experiments for final evaluation. In order to run the code, you need to put features into filefolder "../Features/". To re-implement our results, download features in this link: http://pan.baidu.com/s/1minno6k/.

LICENSE:

Copyright (c) 2016, Zhaoju Li @ Pattern Recognition and Intelligent System Development Laboratory All rights reserved. This package is licensed under BSD Licence.

CITATION:

If you use this code, please kindly cite our work in your publications if it helps your research:

@inproceedings{li2016person,
  title={Person re-identification via adaboost ranking ensemble},
  author={Li, Zhaoju and Han, Zhenjun and Ye, Qixiang},
  booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
  pages={4269--4273},
  year={2016},
  organization={IEEE}
}

If you use oLFDA, kLFDA, LOMO_XQDA, svmml or WHOS in this package, please refer the original paper properly.

  • kLFDA X. Fei, M. Gou, O. Camps and M. Sznaier: "Person Re-Identification using Kernel-based Metric Learning Methods". In ECCV 2014.
  • oLFDA Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: "Local Fisher discriminant analysis for pedestrian re-identification" In CVPR 2013
  • svmml 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
  • LOMO_XQDA Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. "Person re-identification by local maximal occurrence representation and metric learning" In CVPR, 2015.
  • WHOS Lisanti, G. and Masi, I. and Bagdanov, A. and Del Bimbo, A "Person Re-identification by Iterative Re-weighted Sparse Ranking" In PAMI, 2014.

I would like to thank these kind researchers who opened their codes. That helps a lot to those ones like me, especially when I started my research work and built my first framework as a fresh man.

Version: 0.2

  • changelog:

v0.0: raw version

v0.1: add README, delete some irrelevant files.

v0.2: upload features.

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