Datasets:

Voxel51
/

DanceTrack

@@ -61,7 +61,7 @@ dataset_summary: '
 # Dataset Card for DanceTrack
-<!-- Provide a quick summary of the dataset. -->
@@ -98,130 +98,35 @@ session = fo.launch_app(dataset)
 ### Dataset Description
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-- **Curated by:** [More Information Needed]
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 - **Language(s) (NLP):** en
 - **License:** cc-by-4.0
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
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 # Dataset Card for DanceTrack
+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.
 ### Dataset Description
+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.
 - **Language(s) (NLP):** en
 - **License:** cc-by-4.0
+### Dataset Sources
+- **Repository:** https://dancetrack.github.io/
+- **Paper [optional]:** https://arxiv.org/abs/2111.14690
+- **Demo [optional]:** https://dancetrack.github.io/
+## Uses
+This dataset is great for tracking use cases in computer vision is a common benchmark dataset.
+## Citation
+@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}
+}