Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/layumi/Pedestrian_Alignment
TCSVT2018 Pedestrian Alignment Network for Large-scale Person Re-identification
https://github.com/layumi/Pedestrian_Alignment
alignment matconvnet matlab person-re-identification person-recognition person-reid re-identification reid reidentification
Last synced: about 8 hours ago
JSON representation
TCSVT2018 Pedestrian Alignment Network for Large-scale Person Re-identification
- Host: GitHub
- URL: https://github.com/layumi/Pedestrian_Alignment
- Owner: layumi
- License: mit
- Created: 2017-07-02T11:12:40.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-07-21T03:21:16.000Z (over 3 years ago)
- Last Synced: 2024-10-25T04:24:49.019Z (15 days ago)
- Topics: alignment, matconvnet, matlab, person-re-identification, person-recognition, person-reid, re-identification, reid, reidentification
- Language: Cuda
- Homepage: https://ieeexplore.ieee.org/document/8481710
- Size: 5.11 MB
- Stars: 235
- Watchers: 13
- Forks: 66
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Person-Re-Identification - Pedestrian Alignment Network
README
# Pedestrian Alignment Network for Person Re-identification
This repo is for our IEEE TCSVT paper
[[10min Intro Youtube]](https://www.youtube.com/watch?v=OJR43TzS3a8&t=41s&ab_channel=IEEETransonCircuits%26SystemforVideoTech)
(ArXiv Link: https://arxiv.org/abs/1707.00408 IEEE Link: https://ieeexplore.ieee.org/document/8481710).
The main idea is to align the pedestrian within the bboxes, and reduce the noisy factors, i.e., scale and pose variances.
## Network Structure
For more details, you can see this [png file](https://raw.githubusercontent.com/layumi/Pedestrian_Alignment/master/PAN.png). But it is low-solution now, and I may replace it recently.
## Installation
1.Clone this repo.
git clone https://github.com/layumi/Pedestrian_Alignment.git
cd Pedestrian_Alignment
mkdir data
2.Download the pre-trained model. Put it into './data'.
cd data
wget http://www.vlfeat.org/matconvnet/models/imagenet-resnet-50-dag.mat
3.Compile Matconvnet
**(Note that I have included my Matconvnet in this repo, so you do not need to download it again. I have changed some codes comparing with the original version. For example, one of the difference is in `/matlab/+dagnn/@DagNN/initParams.m`. If one layer has params, I will not initialize it again, especially for pretrained model.)**
You just need to uncomment and modify some lines in `gpu_compile.m` and run it in Matlab. Try it~
(The code does not support cudnn 6.0. You may just turn off the Enablecudnn or try cudnn5.1)
If you fail in compilation, you may refer to http://www.vlfeat.org/matconvnet/install/
## Dataset
Download [Market1501 Dataset](http://www.liangzheng.org/Project/project_reid.html). [[Google]](https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view) [[Baidu]](https://pan.baidu.com/s/1ntIi2Op)
For training CUHK03, we follow the new evaluation protocol in the [CVPR2017 paper](https://github.com/zhunzhong07/person-re-ranking). It conducts a multi-shot person re-ID evaluation and only needs to run one time.
## Train
1. Add your dataset path into `prepare_data.m` and run it. Make sure the code outputs the right image path.
2. uncomment https://github.com/layumi/Pedestrian_Alignment/blob/master/resnet52_market.m#L23
Run `train_id_net_res_market_new.m` to pretrain the base branch.
3. comment https://github.com/layumi/Pedestrian_Alignment/blob/master/resnet52_market.m#L23
Run `train_id_net_res_market_align.m` to finetune the whole net.
## Test
1. Run `test/test_gallery_stn_base.m` and `test/test_gallery_stn_align.m` to extract the image features from base branch and alignment brach. Note that you need to change the dir path in the code. They will store in a .mat file. Then you can use it to do the evaluation.
2. Evaluate feature on the Market-1501. Run `evaluation/zzd_evaluation_res_faster.m`. You can get a Single-query Result around the following result.
| Methods | Rank@1 | mAP |
| -------- | ----- | ---- |
| Ours | 82.81% | 63.35% |
You may find our trained model at [GoogleDrive](https://drive.google.com/open?id=1X09jnURIicQk7ivHjVkq55NHPB86hQT0)
## Visualize Results
We conduct an extra interesting experiment:
**When zooming in the input image (adding scale variance), how does our alignment network react?**
We can observe a robust transform on the output image (focusing on the human body and keeping the scale).
The left image is the input; The right image is the output of our network.
## Citation
Please cite this paper in your publications if it helps your research:
```
@article{zheng2017pedestrian,
title={Pedestrian Alignment Network for Large-scale Person Re-identification},
author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},
doi={10.1109/TCSVT.2018.2873599},
note={\mbox{doi}:\url{10.1109/TCSVT.2018.2873599}},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2018}
}
```
## Acknowledge
Thanks for the suggestions from Qiule Sun.