Papers › SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

11 Apr 2023ICCV 2023 1arXiv:2304.05170archive 2025-07-28

Yutao Cui, Chenkai Zeng, Xiaoyu Zhao, Yichun Yang, Gangshan Wu, LiMin Wang

Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as \emph{SportsMOT}, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as \emph{MixSort}, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT. The dataset and code will be available at https://deeperaction.github.io/datasets/sportsmot.html.

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Code

MCG-NJU/SportsMOT officialmentioned on GitHub report

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Tasks

Multi-Object TrackingMultiple Object TrackingObjectObject Tracking

Datasets

Introduced by this paper, per the archive.

SportsMOT

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Object Tracking SportsMOT MixSort-OC AssA 62.0 #10 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-OC DetA 88.5 #10 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-OC HOTA 74.1 #10 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-OC IDF1 74.4 #10 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-OC MOTA 96.5 #10 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-Byte AssA 54.8 #17 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-Byte DetA 78.8 #17 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-Byte HOTA 65.7 #17 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-Byte IDF1 74.1 #17 of 22 Archive leaderboard report
Multi-Object Tracking SportsMOT MixSort-Byte MOTA 96.2 #17 of 22 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-OC AssA 62.0 #9 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-OC DetA 88.5 #9 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-OC HOTA 74.1 #9 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-OC IDF1 74.4 #9 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-OC MOTA 96.5 #9 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-Byte AssA 54.8 #15 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-Byte DetA 78.8 #15 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-Byte HOTA 65.7 #15 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-Byte IDF1 74.1 #15 of 19 Archive leaderboard report
Multiple Object Tracking SportsMOT MixSort-Byte MOTA 96.2 #15 of 19 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAdamBatch NormalizationBigGANConditional Batch NormalizationConvolutionDense ConnectionsEarly StoppingFeedforward NetworkGAN Hinge LossLinear LayerNon-Local BlockNon-Local OperationOff-Diagonal Orthogonal RegularizationProjection DiscriminatorReLUResidual BlockResidual ConnectionSAGANSoftmaxSpectral NormalizationTTURTruncation Trick

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