SportsMOT
홈페이지 · 논문 32편
## Motivation
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.
To this purpose, 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. We expect SportsMOT to encourage the community to concentrate more on the complicated sports scenes.
## Characteristics
- Large scale
- Fine Annotations
- Player id consistency
- No shot change
- High and fixed resolution(1080P)
- ...
## Focus
- Diverse sports scenes
- Complex motion patterns
- Challenging re-id
## Download
### Examples
You can download the example for SportsMOT.
- OneDrive
- Baidu Netdisk, password: 4dnw
### Official Dataset
Please Sign up in codalab, and participate in our competition. Download links are available in Participate/Get Data.
## News
- SportsMOT is used for DeeperAction@ECCV-2022.
- Refer to github repo: MCG-NJU/SportsMOT for the latest info.