p80945617
/
PulseFocusPlatform
forked from PulseFocusPlatform/PulseFocusPlatform
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
PulseFocusPlatform/docs/tutorials/PrepareMOTDataSet.md

12 KiB
Raw Permalink Blame History

English | 简体中文

Contents

Multi-Object Tracking Dataset Preparation

MOT Dataset

PaddleDetection uses the same training data as JDE and FairMOT. Please download and prepare all the training data including Caltech Pedestrian, CityPersons, CUHK-SYSU, PRW, ETHZ, MOT17 and MOT16. MOT15 and MOT20 can also be downloaded from the official webpage of MOT challenge. If you want to use these datasets, please follow their licenses.

Data Format

These several relevant datasets have the following structure:

Caltech
   |——————images
   |        └——————00001.jpg
   |        |—————— ...
   |        └——————0000N.jpg
   └——————labels_with_ids
            └——————00001.txt
            |—————— ...
            └——————0000N.txt
MOT17
   |——————images
   |        └——————train
   |        └——————test
   └——————labels_with_ids
            └——————train

Annotations of these datasets are provided in a unified format. 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]

Notes:

  • class should be 0. Only single-class multi-object tracking is supported now.
  • identity is an integer from 0 to num_identities - 1(num_identities is the total number of instances of objects in the dataset), or -1 if this box has no identity annotation.
  • [x_center] [y_center] [width] [height] are the center coordinates, width and height, note that they are normalized by the width/height of the image, so they are floating point numbers ranging from 0 to 1.

Dataset Directory

First, follow the command below to download the image_list.zip and unzip it in the dataset/mot directory:

wget https://dataset.bj.bcebos.com/mot/image_lists.zip

Then download and unzip each dataset, and the final directory is as follows:

dataset/mot
  |——————image_lists
            |——————caltech.10k.val  
            |——————caltech.all  
            |——————caltech.train  
            |——————caltech.val  
            |——————citypersons.train  
            |——————citypersons.val  
            |——————cuhksysu.train  
            |——————cuhksysu.val  
            |——————eth.train  
            |——————mot15.train  
            |——————mot16.train  
            |——————mot17.train  
            |——————mot20.train  
            |——————prw.train  
            |——————prw.val
  |——————Caltech
  |——————Cityscapes
  |——————CUHKSYSU
  |——————ETHZ
  |——————MOT15
  |——————MOT16
  |——————MOT17
  |——————MOT20
  |——————PRW

Custom Dataset Preparation

In order to standardize training and evaluation, custom data needs to be converted into the same directory and format as MOT-16 dataset:

custom_data
   |——————images
   |        └——————test
   |        └——————train
   |                └——————seq1
   |                |        └——————gt
   |                |        |       └——————gt.txt
   |                |        └——————img1
   |                |        |       └——————000001.jpg
   |                |        |       |——————000002.jpg
   |                |        |       └—————— ...
   |                |        └——————seqinfo.ini
   |                └——————seq2
   |                └——————...
   └——————labels_with_ids
            └——————train
                    └——————seq1
                    |        └——————000001.txt
                    |        |——————000002.txt
                    |        └—————— ...
                    └——————seq2
                    └—————— ...

images

  • gt.txt is the original annotation file of all images extracted from the video.
  • img1 is the folder of images extracted from the video by a certain frame rate.
  • seqinfo.ini is a video information description file, and the following format is required:
[Sequence]
name=MOT16-02
imDir=img1
frameRate=30
seqLength=600
imWidth=1920
imHeight=1080
imExt=.jpg

Each line in gt.txt describes a bounding box, with the format as follows:

[frame_id],[identity],[bb_left],[bb_top],[width],[height],[x],[y],[z]

Notes::

  • frame_id is the current frame id.
  • identity is an integer from 0 to num_identities - 1(num_identities is the total number of instances of objects in the dataset), or -1 if this box has no identity annotation.
  • bb_left is the x coordinate of the left boundary of the target box
  • bb_top is the Y coordinate of the upper boundary of the target box
  • width, height are the pixel width and height
  • x,y,z are only used in 3D, default to -1 in 2D.

labels_with_ids

Annotations of these datasets are provided in a unified format. 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]

Notes:

  • class should be 0. Only single-class multi-object tracking is supported now.
  • identity is an integer from 0 to num_identities - 1(num_identities is the total number of instances of objects in the dataset), or -1 if this box has no identity annotation.
  • [x_center] [y_center] [width] [height] are the center coordinates, width and height, note that they are normalized by the width/height of the image, so they are floating point numbers ranging from 0 to 1.

Generate the corresponding labels_with_ids with following command:

cd dataset/mot
python gen_labels_MOT.py

Download Links

Caltech Pedestrian

Baidu NetDisk: [0] [1] [2] [3] [4] [5] [6] [7]

Google Drive: [annotations] , please download all the images .tar files from this page and unzip the images under Caltech/images

You may need this tool to convert the original data format to jpeg images. Original dataset webpage: CaltechPedestrians

CityPersons

Baidu NetDisk: [0] [1] [2] [3]

Google Drive: [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]

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

Baidu NetDisk: [0]

Google Drive: [0]

Original dataset webpage: MOT-16

MOT-15

Original dataset webpage: MOT-15

MOT-20

Original dataset webpage: MOT-20

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}
}