Repository files navigation

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

Li Zhang

Download data fromhere and unzip itunzip data.zip.

It contains theLOMO feature [1] andkCCA feature [2] for VIPeR dataset.

rundemo.m in Matlab.

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

For LOMO feature, we can get reported result42.28% on VIPeR. (RBF kernel).

For kCCA feature, we can get46.68% (CHI2 kernel),45.92% (RBF kernel).

We can get reported score-level fusion result51% on VIPeR.

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

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}}

• [1]Person Re-identification by Local Maximal Occurrence Representation and Metric Learning.Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li.IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
• [2]Matching people across camera views using kernel canonical correlation analysis.Giuseppe Lisanti, Iacopo Masi, and Alberto Del Bimbo.International Conference on Distributed Smart Cameras (ICDSC), 2014.
• [3]Kernel Null Space Methods for Novelty Detection.Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, and Joachim Denzler.IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.