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.