This repository provides the evaluation codes for the MARS dataset
Matlab C C++
Switch branches/tags
Nothing to show
Clone or download
Latest commit 3a91bbc Jun 13, 2017
Permalink
Failed to load latest commit information.
CM_Curve Add files via upload Aug 9, 2016
KISSME Add files via upload Aug 9, 2016
LOMO_XQDA Add files via upload Aug 9, 2016
info Add files via upload Aug 9, 2016
utils fix the problem of pca Jun 13, 2017
README.md Update README.md Aug 9, 2016
test_mars.m Add files via upload Aug 9, 2016

README.md

MARS-evaluation

This code provides evaluation procedure of the MARS dataset. Please kindly cite the Arxiv paper if you use this dataset.

Liang Zheng*, Zhi Bie*, Yifan Sun*, Jingdong Wang, Chi Su, Shengjin Wang, Qi Tian, "MARS: A Video Benchmark for Large-Scale Person Re-identification", ECCV, 2016. (* equal contribution)

This code uses the 1024-dim IDE descriptor [1] and KISSME [2] and XQDA [3] distance metrics. To run this code, one should follow the three steps below.

  1. Download the pre-computed IDE feature: http://pan.baidu.com/s/1mhBrwMG or https://drive.google.com/folderview?id=0B6tjyrV1YrHed3BnZnNaSUs3eEE&usp=sharing. Unzip it in the root folder.

  2. Run "test_mars.m".

If you want to try your own descriptor or to learn new features, you should do as follows.

  1. Download the dataset: http://pan.baidu.com/s/1hswMDfu or https://drive.google.com/folderview?id=0B6tjyrV1YrHeMVV2UFFXQld6X1E&usp=sharing. Training should be done with images in folder "bbox_train".

  2. Bounding box feature extraction should follow the order specified in "root/info/test_name.txt" and "root/info/train_name.txt." The newly extracted feature should be loaded in line 19-20 in "root/test_mars.m"

If you have any suggestions or comments, please email me at liangzheng06@gmail.com

References

[1] L. Zheng et al. Person Re-identification in the Wild. Arxiv, 2016.

[2] S. Liao et al. Person re-identification by local maximal occurrence representation and metric learning. CVPR 2015.

[3] M. Kostinger et al. Large scale metric learning from equivalence constraints. CVPR 2012.