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Tracking multiple athletes in sports videos is a very challengingMulti-Object Tracking (MOT) task, since athletes often have the same appearanceand are intimately covered with each other, making a common occlusion problembecomes an abhorrent duplicate detection. In this paper, the duplicatedetection is newly and precisely defined as occlusion misreporting on the sameathlete by multiple detection boxes in one frame. To address this problem, wemeticulously design a novel transformer-based Duplicate DetectionDecontaminator (D$^3$) for training, and a specific algorithm Rally-Hungarian(RH) for matching. Once duplicate detection occurs, D$^3$ immediately modifiesthe procedure by generating enhanced boxes losses. RH, triggered by the teamsports substitution rules, is exceedingly suitable for sports videos. Moreover,to complement the tracking dataset that without shot changes, we release a newdataset based on sports video named RallyTrack. Extensive experiments onRallyTrack show that combining D$^3$ and RH can dramatically improve thetracking performance with 9.2 in MOTA and 4.5 in HOTA. Meanwhile, experimentson MOT-series and DanceTrack discover that D$^3$ can accelerate convergenceduring training, especially save up to 80 percent of the original training timeon MOT17. Finally, our model, which is trained only with volleyball videos, canbe applied directly to basketball and soccer videos for MAT, which showspriority of our method. Our dataset is available athttps://github.com/heruihr/rallytrack.

D$^{\bf{3}}$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos