SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
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.
You can download the example for SportsMOT.
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SportsMOT is used for DeeperAction@ECCV-2022.
Refer to github repo: MCG-NJU/SportsMOT for the latest info.
Variants: SportsMOT
This dataset is used in 2 benchmarks:
Recent papers with results on this dataset: