Unsupervised Person Re-identification (ICCV 2017)
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README.md
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README.md

Dynamic Label Graph Matching for Unsupervised Video Re-Identification

Demo code for Dynamic Label Graph Matching for Unsupervised Video Re-Identification in ICCV 2017.

We revised the evaluation protocol for the IDE on MARS dataset. In previous version, due to file traverse problem, which leads a different evaluation protocol, we achieve an extremely high performance (Unsupervised rank-1 65.2%, and supervised 75.8%) compared with other baselines in our cv-foundation version. We re-evaluate our perfomance under standard settings, the rank-1 is 36.8% for our unsupervised method, and the supervised upper bound is 56.2%. Please refer to the version on our website and github for latest results. PDF

1. Test on PRID-2011 and iLIDS-VID datasets.

  • a. You need to download our extracted features LOMO on BaiduYun and GoogleDrive or extract features by yourself. Put it under "data/" folder

  • b. You could run the demo_dgm.m and edit it to adjust for different datsets and different settings.

Results

  • LOMO on PRID-2011 and iLIDS-VID
Datasets Rank@1 Rank@5 Rank@10
#PRID-2011 73.1% 92.5% 96.7%
#iLIDS-VID 37.1% 61.3% 72.2%

Results

  • On MARS dataset
Methods Rank@1 Rank@5 mAP
#LOMO 24.6% 42.6% 11.8%
#IDE 36.8% 54.0% 21.3%

Citation

Please cite this paper in your publications if it helps your research:

@inproceedings{iccv17dgm,
  title={Dynamic Label Graph Matching for Unsupervised Video Re-Identification},
  author={Ye, Mang and Ma, Andy J and Zheng, Liang and Li, Jiawei and Yuen, Pong C.},
  booktitle={ICCV},
  year={2017},
}

Contact: mangye@comp.hkbu.edu.hk