README.md

PoseTrack21

Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e.g. person detection or appearance-based association of detected persons. Consequently, approaches on these respective tasks are eligible to complement each other. Therefore, we introduce PoseTrack21, a large-scale dataset for person search, multi-object tracking and multi-person pose tracking in real-world scenarios with a high diversity of poses. The dataset provides rich annotations like human pose annotations including annotations of joint occlusions, bounding box annotations even for small persons, and person-ids within and across video sequences. The dataset allows to evaluate multi-object tracking and multi-person pose tracking jointly with person re-identification or exploit structural knowledge of human poses to improve person search and tracking, particularly in the context of severe occlusions. With PoseTrack21, we want to encourage researchers to work on joint approaches that perform reasonably well on all three tasks.

How to get the dataset?

In order to obtain the entire dataset, please fill out this document and send it to posetrack21[at]googlegroups[dot]com. Please note that we require a (digitally) handwritten signature.

NOTE: Please don't request write access to the template of the agreement sheet. Download the agreement sheet and fill it locally on your computer and send it to us.

Afterwards, please run the following command with you access token:

python3 download_dataset.py --save_path /target/root/path/of/the/dataset --token="[your token]"

!!NOTE!!: If you are having problems downloading the dataset, please follow these instructions.

Structure of the dataset

The dataset is organized as follows:

.
├── images                              # contains all images  
    ├── train
    ├── val
├── posetrack_data                      # contains annotations for pose reid tracking
    ├── train
        ├── 000001_bonn_train.json
        ├── ...
    ├── val
        ├── ...
├── posetrack_mot                       # contains annotations for multi-object tracking 
    ├── mot
        ├── train
            ├── 000001_bonn_train
                ├── image_info.json
                ├── gt
                    ├── gt.txt          # ground truth annotations in mot format
                    ├── gt_kpts.txt     # ground truth poses for each frame
            ├── ...
        ├── val
├── posetrack_person_search             # person search annotations
    ├── query.json
    ├── train.json
    ├── val.json

A detailed description of the respective dataset formats can be found here.

Usage

Instructions on the evaluation of the respective tacks are provided here.

Citation

If you are using our dataset for your research, please cite our paper.

@inproceedings{doering22,
  title={Pose{T}rack21: {A} Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking},
  author={Andreas Doering and Di Chen and Shanshan Zhang and Bernt Schiele and Juergen Gall},
  booktitle={CVPR},
  year={2022}
}

Since PoseTrack21 uses the videos from PoseTrack18, please cite their work as well.

@inproceedings{andriluka18,
	Title = {Pose{T}rack: {A} Benchmark for Human Pose Estimation and Tracking},
	booktitle = {CVPR},
	Author = {Andriluka, M. and Iqbal, U. and Ensafutdinov, E. and Pishchulin, L. and Milan, A. and Gall, J. and Schiele B.},
	Year = {2018}
}