Skip to content

liangzheng06/MARS-evaluation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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.

About

This repository provides the evaluation codes for the MARS dataset

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published