6.8 KiB
Dataset Zoo
We provide several relevant datasets for training and evaluating the Joint Detection and Embedding (JDE) model. Annotations are provided in a unified format. If you want to use these datasets, please follow their licenses, and if you use these datasets in your research, please cite the original work (you can find the bibtex in the bottom).
Data Format
All the dataset has the following structrue:
Caltech
|——————images
| └——————00001.jpg
| |—————— ...
| └——————0000N.jpg
└——————labels_with_ids
└——————00001.txt
|—————— ...
└——————0000N.txt
Every image corresponds to an annation text. Given an image path,
the annotation text path can be easily generated by replacing the string images with labels_with_ids and replacing .jpg with .txt.
In the annotation text, each line is a bounding box and has the following format,
[class] [identity] [x_center] [y_center] [width] [height]
The field [class] is not used in this project since we only care about a single class, i.e., pedestrian here.
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 float numbers ranging from 0 to 1.
Download
Caltech Pedestrian
Baidu NetDisk: [0] [1] [2] [3] [4] [5] [6] [7]
Google Drive: [annotation] ,
please download all the .tar file from this page and unzip the images under Caltech/images
Original dataset webpage: CaltechPedestrians
CityPersons
Baidu NetDisk: [0] [1] [2] [3]
Original dataset webpage: Citypersons pedestrian detection dataset
CUHK-SYSU
Baidu NetDisk: [0]
Google Drive: [0]
Original dataset webpage: CUHK-SYSU Person Search Dataset
PRW
Baidu NetDisk: [0]
Google Drive: [0]
Original dataset webpage: Person Search in the Wild datset
ETHZ (overlapping videos with MOT-16 removed):
Baidu NetDisk: [0]
Google Drive: [0]
Original dataset webpage: ETHZ pedestrian datset
MOT-17
Baidu NetDisk: [0]
Google Drive: [0]
Original dataset webpage: MOT-17
MOT-16 (for evaluation )
Baidu NetDisk: [0]
Google Drive: [0]
Original dataset webpage: MOT-16
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}
}