PrepareMOTDataSet.md

# MOT Dataset
* **MIXMOT**
We use the same training data as [JDE](https://github.com/Zhongdao/Towards-Realtime-MOT) and [FairMOT](https://github.com/ifzhang/FairMOT) in this part and we call it "MIXMOT". Please refer to their [DATA ZOO](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) to download and prepare all the training data including Caltech Pedestrian, CityPersons, CUHK-SYSU, PRW, ETHZ, MOT17 and MOT16.

* **2DMOT15 and MOT20**
[2DMOT15](https://motchallenge.net/data/2D_MOT_2015/) and [MOT20](https://motchallenge.net/data/MOT20/) can be downloaded from the official webpage of MOT challenge. After downloading, you should prepare the data in the following structure:
```
MOT15
   |——————images
   |        └——————train
   |        └——————test
   └——————labels_with_ids
            └——————train
MOT20
   |——————images
   |        └——————train
   |        └——————test
   └——————labels_with_ids
            └——————train
```
Annotations of these several relevant datasets are provided in a unified format. If you want to use these datasets, please **follow their licenses**,
and if you use any of these datasets in your research, please cite the original work (you can find the BibTeX in the bottom).
## Data Format
All the datasets have the following structure:
```
Caltech
   |——————images
   |        └——————00001.jpg
   |        |—————— ...
   |        └——————0000N.jpg
   └——————labels_with_ids
            └——————00001.txt
            |—————— ...
            └——————0000N.txt
```
Every image has a corresponding annotation text. Given an image path,
the annotation text path can be generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.

In the annotation text, each line is describing a bounding box and has the following format:
```
[class] [identity] [x_center] [y_center] [width] [height]
```
The field `[class]` should be `0`. Only single-class multi-object tracking is supported in this version.

The field `[identity]` is an integer from `0` to `num_identities - 1`, or `-1` if this box has no identity annotation.

***Note** that the values of `[x_center] [y_center] [width] [height]` are normalized by the width/height of the image, so they are floating point numbers ranging from 0 to 1.

## Final Dataset root
```
dataset/mot
  |——————image_lists
            |——————caltech.10k.val  
            |——————caltech.train  
            |——————caltech.val  
            |——————citypersons.train  
            |——————citypersons.val  
            |——————cuhksysu.train  
            |——————cuhksysu.val  
            |——————eth.train  
            |——————mot16.train  
            |——————mot17.train  
            |——————prw.train  
            |——————prw.val
  |——————Caltech
  |——————Cityscapes
  |——————CUHKSYSU
  |——————ETHZ
  |——————MOT15
  |——————MOT16
  |——————MOT17
  |——————MOT20
  |——————PRW
```

## Download

### Caltech Pedestrian
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)

Google Drive: [[annotations]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing) ,
please download all the images `.tar` files from [this page](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/) and unzip the images under `Caltech/images`

You may need [this tool](https://github.com/mitmul/caltech-pedestrian-dataset-converter) to convert the original data format to jpeg images.
Original dataset webpage: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
### CityPersons
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)

Google Drive:
[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)

Original dataset webpage: [Citypersons pedestrian detection dataset](https://bitbucket.org/shanshanzhang/citypersons)

### CUHK-SYSU
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)

Google Drive:
[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)

Original dataset webpage: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)

### PRW
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)

Google Drive:
[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)

Original dataset webpage: [Person Search in the Wild datset](http://www.liangzheng.com.cn/Project/project_prw.html)

### ETHZ (overlapping videos with MOT-16 removed):
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)

Google Drive:
[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)

Original dataset webpage: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)

### MOT-17
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)

Google Drive:
[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)

Original dataset webpage: [MOT-17](https://motchallenge.net/data/MOT17/)

### MOT-16 (for evaluation )
Baidu NetDisk:
[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)

Google Drive:
[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)

Original dataset webpage: [MOT-16](https://motchallenge.net/data/MOT16/)


# Citation
Caltech:
```
@inproceedings{ dollarCVPR09peds,
       author = "P. Doll\'ar and C. Wojek and B. Schiele and  P. Perona",
       title = "Pedestrian Detection: A Benchmark",
       booktitle = "CVPR",
       month = "June",
       year = "2009",
       city = "Miami",
}
```
Citypersons:
```
@INPROCEEDINGS{Shanshan2017CVPR,
  Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
  Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
  Booktitle = {CVPR},
  Year = {2017}
 }

@INPROCEEDINGS{Cordts2016Cityscapes,
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}
}
```
CUHK-SYSU:
```
@inproceedings{xiaoli2017joint,
  title={Joint Detection and Identification Feature Learning for Person Search},
  author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
  booktitle={CVPR},
  year={2017}
}
```
PRW:
```
@inproceedings{zheng2017person,
  title={Person re-identification in the wild},
  author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1367--1376},
  year={2017}
}
```
ETHZ:
```
@InProceedings{eth_biwi_00534,
author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
title = {A Mobile Vision System for Robust Multi-Person Tracking},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
year = {2008},
month = {June},
publisher = {IEEE Press},
keywords = {}
}
```
MOT-16&17:
```
@article{milan2016mot16,
  title={MOT16: A benchmark for multi-object tracking},
  author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
  journal={arXiv preprint arXiv:1603.00831},
  year={2016}
}
```