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DanceTrack is a multi-human tracking dataset with two emphasized properties, (1) uniform appearance: humans are in highly similar and almost undistinguished appearance, (2) diverse motion: humans are in complicated motion pattern and their relative positions exchange frequently. We expect the combination of uniform appearance and complicated motion pattern makes DanceTrack a platform to encourage more comprehensive and intelligent multi-object tracking algorithms.
This is a FiftyOne dataset with 33 samples.
If you haven't already, install FiftyOne:
pip install -U fiftyone
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'split', 'max_samples', etc
dataset = fouh.load_from_hub("dgural/DanceTrack")
# Launch the App
session = fo.launch_app(dataset)
From Multi-Object Tracking in Uniform Appearance and Diverse Motion:
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is partially motivated by recent progress in both object detec- tion and re-ID, and partially motivated by biases in existing tracking datasets, where most objects tend to have distin- guishing appearance and re-ID models are sufficient for es- tablishing associations. In response to such bias, we would like to re-emphasize that methods for multi-object tracking should also work when object appearance is not sufficiently discriminative. To this end, we propose a large-scale dataset for multi-human tracking, where humans have similar appearance, diverse motion and extreme articulation. As the dataset contains mostly group dancing videos, we name it “DanceTrack”. We expect DanceTrack to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis. We benchmark several state-of-the-art trackers on our dataset and observe a significant performance drop on DanceTrack when compared against existing benchmarks.
This dataset is great for tracking use cases in computer vision is a common benchmark dataset.
@inproceedings{sun2022dance, title={DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion}, author={Sun, Peize and Cao, Jinkun and Jiang, Yi and Yuan, Zehuan and Bai, Song and Kitani, Kris and Luo, Ping}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022} }