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
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a. You need to download our extracted features LOMO on BaiduYun and GoogleDrive or extract features by yourself. Put it under "data/" folder
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b. You could run the demo_dgm.m and edit it to adjust for different datsets and different settings.
- 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% |
- On MARS dataset
| Methods | Rank@1 | Rank@5 | mAP |
|---|---|---|---|
| #LOMO | 24.6% | 42.6% | 11.8% |
| #IDE | 36.8% | 54.0% | 21.3% |
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