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--- |
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annotations_creators: [] |
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language: en |
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license: cc-by-4.0 |
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task_categories: [] |
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task_ids: [] |
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pretty_name: DanceTrack |
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tags: |
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- fiftyone |
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- video |
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chunk_size: 1 |
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dataset_summary: ' |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33 samples. |
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## Installation |
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If you haven''t already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include ''split'', ''max_samples'', etc |
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dataset = fouh.load_from_hub("voxel51/DanceTrack") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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' |
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--- |
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# Dataset Card for DanceTrack |
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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. |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33 samples. |
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## Installation |
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If you haven't already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include 'split', 'max_samples', etc |
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dataset = fouh.load_from_hub("dgural/DanceTrack") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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## Dataset Details |
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### Dataset Description |
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From _Multi-Object Tracking in Uniform Appearance and Diverse Motion_: |
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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. |
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- **Language(s) (NLP):** en |
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- **License:** cc-by-4.0 |
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### Dataset Sources |
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- **Repository:** https://dancetrack.github.io/ |
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- **Paper [optional]:** https://arxiv.org/abs/2111.14690 |
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- **Demo [optional]:** https://dancetrack.github.io/ |
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## Uses |
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This dataset is great for tracking use cases in computer vision is a common benchmark dataset. |
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## Citation |
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@inproceedings{sun2022dance, |
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title={DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion}, |
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author={Sun, Peize and Cao, Jinkun and Jiang, Yi and Yuan, Zehuan and Bai, Song and Kitani, Kris and Luo, Ping}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year={2022} |
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} |
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