无需训练即可实现体育视频中多目标长期跟踪，提升精度与速度。
Training-Free Long-Term Multi-Object Tracking for Sports Video Analytics
- 不依赖训练，融合掩码传播与相机运动补偿的统一追踪框架。
- 在SoccerNet和SportsMOT上比原版提升3.0点HOTA和6.1点IDF1。
- 速度提升近10倍，适合实时体育分析场景使用。
体育视频中的长期多目标跟踪仍面临频繁遮挡、快速相机运动和球员重复出现等挑战。我们提出McByte++，一种无需训练的检测后追踪框架，整合轻量级掩码传播、条件相机运动补偿与在线重识别，形成统一流程。相比原版，McByte++显著提升运行效率并增强身份保持能力。在SoccerNet-tracking和SportsMOT基准上，其在线设置下相较原始McByte最高提升3.0点HOTA和6.1点IDF1，结合离线全局关联可进一步增益。通过替换重型分割模块并优化运动建模，速度最高提升一个数量级。所有结果均无需检测器重训练或数据集特化调优。代码将发布于https://github.com/tstanczyk95/McBytePlusPlus。
原文摘要 · Abstract (English)
Long-term multi-object tracking in sports remains challenging due to frequent occlusions, rapid camera motion, and repeated player reappearances. We introduce McByte++, a training-free tracking-by-detection framework that integrates lightweight mask propagation, conditional camera motion compensation, and online re-identification within a unified pipeline. Compared to its predecessor, McByte++ substantially improves runtime efficiency while enhancing identity preservation. On SoccerNet-tracking and SportsMOT benchmarks, McByte++ achieves up to +3.0 HOTA and +6.1 IDF1 improvements over the original McByte in the online setting, with further gains when combined with offline global association. Replacing heavy segmentation components and optimizing motion modeling yields up to an order-of-magnitude speed increase. All results are obtained without detector retraining or dataset-specific tuning. Code will be made available at https://github.com/tstanczyk95/McBytePlusPlus.
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