Datasets:

Modalities:
Video
Languages:
English
ArXiv:
Libraries:
FiftyOne
License:
dgural commited on
Commit
95c11c2
1 Parent(s): 4c1d878

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +17 -112
README.md CHANGED
@@ -61,7 +61,7 @@ dataset_summary: '
61
 
62
  # Dataset Card for DanceTrack
63
 
64
- <!-- Provide a quick summary of the dataset. -->
65
 
66
 
67
 
@@ -98,130 +98,35 @@ session = fo.launch_app(dataset)
98
 
99
  ### Dataset Description
100
 
101
- <!-- Provide a longer summary of what this dataset is. -->
 
 
102
 
103
 
104
 
105
- - **Curated by:** [More Information Needed]
106
- - **Funded by [optional]:** [More Information Needed]
107
- - **Shared by [optional]:** [More Information Needed]
108
  - **Language(s) (NLP):** en
109
  - **License:** cc-by-4.0
110
 
111
- ### Dataset Sources [optional]
112
-
113
- <!-- Provide the basic links for the dataset. -->
114
-
115
- - **Repository:** [More Information Needed]
116
- - **Paper [optional]:** [More Information Needed]
117
- - **Demo [optional]:** [More Information Needed]
118
-
119
- ## Uses
120
-
121
- <!-- Address questions around how the dataset is intended to be used. -->
122
-
123
- ### Direct Use
124
-
125
- <!-- This section describes suitable use cases for the dataset. -->
126
-
127
- [More Information Needed]
128
-
129
- ### Out-of-Scope Use
130
-
131
- <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
132
-
133
- [More Information Needed]
134
-
135
- ## Dataset Structure
136
-
137
- <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
138
-
139
- [More Information Needed]
140
-
141
- ## Dataset Creation
142
-
143
- ### Curation Rationale
144
-
145
- <!-- Motivation for the creation of this dataset. -->
146
-
147
- [More Information Needed]
148
-
149
- ### Source Data
150
-
151
- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
152
-
153
- #### Data Collection and Processing
154
-
155
- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
156
-
157
- [More Information Needed]
158
-
159
- #### Who are the source data producers?
160
 
161
- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
162
 
163
- [More Information Needed]
164
 
165
- ### Annotations [optional]
 
 
166
 
167
- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
168
-
169
- #### Annotation process
170
-
171
- <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
172
-
173
- [More Information Needed]
174
-
175
- #### Who are the annotators?
176
-
177
- <!-- This section describes the people or systems who created the annotations. -->
178
-
179
- [More Information Needed]
180
-
181
- #### Personal and Sensitive Information
182
-
183
- <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
184
-
185
- [More Information Needed]
186
-
187
- ## Bias, Risks, and Limitations
188
-
189
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
190
-
191
- [More Information Needed]
192
-
193
- ### Recommendations
194
-
195
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
196
-
197
- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
198
-
199
- ## Citation [optional]
200
-
201
- <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
202
-
203
- **BibTeX:**
204
-
205
- [More Information Needed]
206
-
207
- **APA:**
208
-
209
- [More Information Needed]
210
-
211
- ## Glossary [optional]
212
-
213
- <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
214
-
215
- [More Information Needed]
216
 
217
- ## More Information [optional]
218
 
219
- [More Information Needed]
220
 
221
- ## Dataset Card Authors [optional]
222
 
223
- [More Information Needed]
224
 
225
- ## Dataset Card Contact
 
 
 
 
 
226
 
227
- [More Information Needed]
 
61
 
62
  # Dataset Card for DanceTrack
63
 
64
+ 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.
65
 
66
 
67
 
 
98
 
99
  ### Dataset Description
100
 
101
+ From _Multi-Object Tracking in Uniform Appearance and Diverse Motion_:
102
+
103
+ 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.
104
 
105
 
106
 
 
 
 
107
  - **Language(s) (NLP):** en
108
  - **License:** cc-by-4.0
109
 
110
+ ### Dataset Sources
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
111
 
 
112
 
 
113
 
114
+ - **Repository:** https://dancetrack.github.io/
115
+ - **Paper [optional]:** https://arxiv.org/abs/2111.14690
116
+ - **Demo [optional]:** https://dancetrack.github.io/
117
 
118
+ ## Uses
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
119
 
120
+ This dataset is great for tracking use cases in computer vision is a common benchmark dataset.
121
 
 
122
 
 
123
 
124
+ ## Citation
125
 
126
+ @inproceedings{sun2022dance,
127
+ title={DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion},
128
+ author={Sun, Peize and Cao, Jinkun and Jiang, Yi and Yuan, Zehuan and Bai, Song and Kitani, Kris and Luo, Ping},
129
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
130
+ year={2022}
131
+ }
132