Awesome

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