Skip to content
Demo code for CVPR 2016 paper: Learning a Discriminative Null Space for Person Re-identification
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
figure
DNS.m
RBF_kernel.m
RBF_kernel2.m
README.md
demo.m
evaluateCMC_demo.m
kernel_expchi2.m
plotCMCcurve.m
plotCurrentTrial.m

README.md

Discriminative Null Space for Person Re-ID

Demo code for CVPR 2016 paper: Learning a Discriminative Null Space for Person Re-identification

Li Zhang

Data

Download data from here and unzip it unzip data.zip.

It contains the LOMO feature [1] and kCCA feature [2] for VIPeR dataset.

Run

run demo.m in Matlab.

Results

We used the VIPeR data split provided by [2] in https://github.com/glisanti/KCCAReId.

For LOMO feature, we can get reported result 42.28% on VIPeR. (RBF kernel).

For kCCA feature, we can get 46.68% (CHI2 kernel), 45.92% (RBF kernel).

We can get reported score-level fusion result 51% on VIPeR.

CMC curve

Download the CMC curve on VIPeR, PRID, CUHK01, CUHK03 and Market1501 from here.

Citing

If you use this code in your research, please use the following BibTeX entry.

@inproceedings{zhang2016learning,
  title={Learning a discriminative null space for person re-identification},
  author={Zhang, Li and Xiang, Tao and Gong, Shaogang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2016}
}

References

You can’t perform that action at this time.