Person Re-ranking (CVPR 2017)
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python-version Rename re_ranking to re_ranking_feature Jan 8, 2018
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README.md

Re-ranking Person Re-identification with k-reciprocal Encoding

================================================================

This code has the source code for the paper "Re-ranking Person Re-identification with k-reciprocal Encoding". Including:

  1. IDE baseline
  2. Re-ranking code
  3. CUHK03 new training/testing protocol

If you find this code useful in your research, please consider citing:

@article{zhong2017re,
  title={Re-ranking Person Re-identification with k-reciprocal Encoding},
  author={Zhong, Zhun and Zheng, Liang and Cao, Donglin and Li, Shaozi},
  booktitle={CVPR},
  year={2017}
}

The neighbor encoding method of our paper is inspired by the reference [2]. If you use the re-ranking code in your paper, please also cite:

@article{bai2016sparse,
  title={Sparse contextual activation for efficient visual re-ranking},
  author={Bai, Song and Bai, Xiang},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  publisher={IEEE}
}

================================================================

Two python version of re-ranking fcuntions are added in the 'python' folder.

  1. re_ranking_feature.py: re-ranking with original feature, Euclidean distance is used. Thanks Hao Luo !
  2. re_ranking_ranklist: re-ranking with given distance matrices, handle the difference of / division between python 2 and 3. Thanks huang houjing !

Pytorch re-implementation

[Baseline + CamStyle + Random Erasing + Re-ranking]

[Person_reID_baseline + Random Erasing + Re-ranking]

================================================================

The new training/testing protocol for CUHK03

[Dataset and state-of-the-art]

================================================================

IDE Baseline + Re-ranking

Requirements: Caffe

Requirements for Caffe and matcaffe (see: Caffe installation instructions)

Installation

  1. Build Caffe and matcaffe

    cd $Re-ranking_ROOT/caffe
    # Now follow the Caffe installation instructions here:
    # http://caffe.berkeleyvision.org/installation.html
    make -j8 && make matcaffe
  2. Download pre-computed imagenet models, Market-1501 dataset and CUHK03 dataset

Please download the pre-trained imagenet models and put it in the "data/imagenet_models" folder.
Please download Market-1501 dataset and unzip it in the "evaluation/data/Market-1501" folder. 
Please download CUHK03 dataset and unzip it in the "evaluation/data/CUHK03" folder.

Training and testing IDE model

  1. Training
cd $Re-ranking_ROOT
# train IDE ResNet_50 for Market-1501
./experiments/Market-1501/train_IDE_ResNet_50.sh

# train IDE ResNet_50 for CUHK03
./experiments/CUHK03/train_IDE_ResNet_50_labeled.sh
./experiments/CUHK03/train_IDE_ResNet_50_detected.sh
  1. Feature Extraction
cd $Re-ranking_ROOT/evaluation
# extract feature for Market-1501
matlab Market_1501_extract_feature.m

# extract feature for CUHK03
matlab CUHK03_extract_feature.m
  1. Evaluation
# evaluation for Market-1501
matlab Market_1501_evaluation.m
  
# evaluation for CUHK03
matlab CUHK03_evaluation.m

Results

You can download our pre-trained IDE models and IDE features, and put them in the "output" and "evaluation/feat" folder, respectively.

Using the above IDE models and IDE features, you can reproduce the results with our re-ranking method as follows:

  • Market-1501
Methods   Rank@1 mAP
IDE_ResNet_50 + Euclidean 78.92% 55.03%
IDE_ResNet_50 + Euclidean + re-ranking 81.44% 70.39%
IDE_ResNet_50 + XQDA 77.58% 56.06%
IDE_ResNet_50 + XQDA + re-ranking 80.70% 69.98%

For Market-1501, these results are better than those reported in our paper, since we add a dropout = 0.5 layer after pool5.

  • CUHK03 under the new training/testing protocol
Labeled Labeled detected detected
Methods Rank@1 mAP Rank@1 mAP
BOW + XQDA [1] 7.93% 7.29% 6.36% 6.39%
BOW + XQDA + re-ranking 8.93% 9.94% 8.29% 8.81%
LOMO + XQDA [3] 14.8% 13.6% 12.8% 11.5%
LOMO + XQDA + re-ranking 19.1% 20.8% 16.6% 17.8%
IDE_CaffeNet + Euclidean 15.6% 14.9% 15.1% 14.2%
IDE_CaffeNet + Euclidean + re-ranking 19.1% 21.3% 19.3% 20.6%
IDE_CaffeNet + XQDA 21.9% 20.0% 21.1% 19.0%
IDE_CaffeNet + XQDA + re-ranking 25.9% 27.8% 26.4% 26.9%
IDE_ResNet_50 + Euclidean 22.2% 21.0% 21.3% 19.7%
IDE_ResNet_50 + Euclidean + re-ranking 26.6% 28.9% 24.9% 27.3%
IDE_ResNet_50 + XQDA 32.0% 29.6% 31.1% 28.2%
IDE_ResNet_50 + XQDA + re-ranking 38.1% 40.3% 34.7% 37.4%

References

[1] Scalable Person Re-identification: A Benchmark. Zheng, Liang and Shen, Liyue and Tian, Lu and Wang, Shengjin and Wang, Jingdong and Tian, Qi. In ICCV 2015.

[2] Sparse contextual activation for efficient visual re-ranking. Bai, Song and Bai, Xiang. IEEE Transactions on Image Processing. 2016

[3] Person re-identification by local maximal occurrence representation and metric learning. Liao S, Hu Y, Zhu X, et al. In CVPR. 2015

Contact us

If you have any questions about this code, please do not hesitate to contact us.

Zhun Zhong

Liang Zheng