Update README.md

README.md

@@ -1,3 +1,142 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+annotations_creators:
+- crowdsourced
+task_categories:
+- object-detection
+- other
+language:
+- en
+tags:
+- video
+- multi-object tracking
+pretty_name: SportsMOT
+source_datasets:
+- MultiSports
+extra_gated_heading: "Acknowledge license to accept the repository"
+extra_gated_prompt: "This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License"
+extra_gated_fields:
+  Institute: text
+  I want to use this dataset for:
+    type: select
+    options:
+      - Research
+      - Education
+      - label: Other
+        value: other
+  I agree to use this dataset for non-commerical use ONLY: checkbox
+---
+# Dataset Card for SportsMOT
+
+<!-- Provide a quick summary of the dataset. -->
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+Multi-object tracking (MOT) is a fundamental task in computer vision, aiming to estimate objects (e.g., pedestrians and vehicles) bounding boxes and identities in video sequences. We propose a large-scale multi-object tracking dataset named SportsMOT, consisting of 240 video clips from 3 categories (i.e., basketball, football and volleyball). The objective is to only track players on the playground (i.e., except for a number of spectators, referees and coaches) in various sports scenes.
+
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/MCG-NJU/SportsMOT
+- **Paper:** https://arxiv.org/abs/2304.05170
+- **Competiton:** https://codalab.lisn.upsaclay.fr/competitions/12424
+- **Point of Contact:** mailto: yichunyang@smail.nju.edu.cn
+
+
+## Dataset Structure
+
+<!-- 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. -->
+
+Data in SportsMOT is organized in the form of MOT Challenge 17.
+
+```
+  splits_txt(video-split mapping)
+    - basketball.txt
+    - volleyball.txt
+    - football.txt
+    - train.txt
+    - val.txt
+    - test.txt
+  scripts
+    - mot_to_coco.py
+    - sportsmot_to_trackeval.py
+  dataset(in MOT challenge format)
+    - train
+      - VIDEO_NAME1
+        - gt
+        - img1
+          - 000001.jpg
+          - 000002.jpg
+        - seqinfo.ini
+    - val(the same hierarchy as train)
+    - test
+      - VIDEO_NAME1
+        - img1
+          - 000001.jpg
+          - 000002.jpg
+        - seqinfo.ini
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+Multi-object tracking (MOT) is a fundamental task in computer vision, aiming to estimate objects (e.g., pedestrians and vehicles) bounding boxes and identities in video sequences.
+
+Prevailing human-tracking MOT datasets mainly focus on pedestrians in crowded street scenes (e.g., MOT17/20) or dancers in static scenes (DanceTrack). In spite of the increasing demands for sports analysis, there is a lack of multi-object tracking datasets for a variety of sports scenes, where the background is complicated, players possess rapid motion and the camera lens moves fast.
+
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+> We select three worldwide famous sports, football, basketball, and volleyball, and collect videos of high-quality professional games including NCAA, Premier League, and Olympics from MultiSports, which is a large dataset in sports area focusing on spatio-temporal action localization.
+
+#### Annotation process
+
+<!-- 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. -->
+
+We annotate the collected videos according to the following guidelines.
+
+1. The entire athlete’s limbs and torso, excluding any other objects like balls touching the athlete’s body, are required to be annotated.
+
+2. The annotators are asked to predict the bounding box of the athlete in the case of occlusion, as long as the athletes have a visible part of body. However, if half of the athletes’ torso is outside the view, annotators should just skip them.
+
+3. We ask the annotators to confirm that each player has a unique ID throughout the whole clip.
+
+### Dataset Curators
+
+Authors of [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://arxiv.org/pdf/2304.05170)
+
+- Yutao Cui
+
+- Chenkai Zeng
+
+- Xiaoyu Zhao
+
+- Yichun Yang
+
+- Gangshan Wu
+
+- Limin Wang
+
+## Citation Information
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+If you find this dataset useful, please cite as
+
+```
+@inproceedings{cui2023sportsmot,
+  title={Sportsmot: A large multi-object tracking dataset in multiple sports scenes},
+  author={Cui, Yutao and Zeng, Chenkai and Zhao, Xiaoyu and Yang, Yichun and Wu, Gangshan and Wang, Limin},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={9921--9931},
+  year={2023}
+}
+```