Dataset Zoo
Datasets preparing was used from Towards-Realtime-MOT
Data Format
The root folder of datasets will have the following structure:
.
└─datasets
├─Caltech
├─Cityscapes
├─CUHKSYSU
├─ETHZ
├─MOT16
├─MOT17
└─PRW
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.
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.
Download
Caltech Pedestrian
Download all archives set**.tar
files from this page and extract to Caltech/data
.
Download annotations and unzip to Caltech/data/labels_with_ids
.
Download this tool to convert the original data format to images.
Move scripts
folder of tool to Caltech
folder and use command:
python scripts/convert_seqs.py
The structure of the dataset after completing all steps will be the following:
.
└─Caltech
└─data
├─images
│ └─***
└─labels_with_ids
└─***
Note: *** - it is a data (images or annotations)
CityPersons
Google Drive:
[0]
[1]
[2]
[3]
Download .zip
archives from links and use the following commands.
zip --FF Citypersons --out c.zip
unzip c.zip
mv Citypersons Cityscapes
The structure of the dataset after completing all steps will be the following:
.
└─Cityscapes
├─images
│ ├─train
│ └─val
└─labels_with_ids
├─train
└─val
CUHK-SYSU
Google Drive:
[0]
Original dataset webpage: CUHK-SYSU Person Search Dataset
Download dataset, unzip and use command below.
mv CUHK-SYSU CUHKSYSU
The structure of the dataset will be the following:
.
└─CUHKSYSU
├─images
│ └─***
└─labels_with_ids
└─***
Note: *** - it is a data (images or annotations)
PRW
Google Drive:
[0]
Download dataset and unzip. The structure of the dataset will be the following:
.
└─PRW
├─images
│ └─***
└─labels_with_ids
└─***
Note: *** - it is a data (images or annotations)
ETHZ (overlapping with MOT-16 removed)
Google Drive:
[0]
Original dataset webpage: ETHZ pedestrian dataset
Download dataset and unzip. The structure of the dataset will be the following:
.
└─ETHZ
├─eth01
│ ├─images
│ │ └─***
│ └─labels_with_ids
│ └─***
├─eth02
├─eth03
├─eth05
└─eth07
Note: *** - it is a data (images or annotations). Same structure to every 'eth*' folder.
MOT-17
Official link:
[0]
Original dataset webpage: MOT-17
After downloading, unzip and use prepare_mot17.py
script from the:
python data/prepare_mot17.py --seq_root /path/to/MOT17/train
The structure of the dataset after completing all steps will be the following:
.
└─MOT17
├─images
│ └─train
└─labels_with_ids
└─train
MOT-16 (for evaluation)
Google Drive:
[0]
Original dataset webpage: MOT-16
Download link: MOT-16.zip
The section "Download" in the bottom of the web-page. Link: "Get all data".
Download dataset and unzip. The structure of the dataset will be the following:
.
└─MOT16
└─train
Data config
Download schemas of the training data with relative paths for every image, divided into train/val parts and move into data
folder.
.
└── data
├─ caltech.10k.val
├─ caltech.train
├─ caltech.val
├─ citypersons.train
├─ citypersons.val
├─ cuhksysu.train
├─ cuhksysu.val
├─ eth.train
├─ mot17.train
├─ prw.train
└─ prw.val
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},
}
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}
}