用时序传播的分割掩码提升多目标追踪精度与泛化能力
Temporally Propagated Masks and Bounding Boxes: Combining the Best of Both Worlds for Multi-Object Tracking
- 在检测式追踪框架中引入时序传播的分割掩码作为关联线索
- 四个基准数据集上均实现性能提升，超越现有掩码方法
- 无需每序列调参，适合实际部署场景
多目标追踪（MOT）旨在视频序列中识别并持续跟踪多个对象。传统检测式方法虽有效，但需大量调参且泛化能力差；基于分割掩码的方法更通用，却难以管理追踪关系，不适用于MOT。本文提出McByte，将时序传播的分割掩码作为强关联线索融入检测式追踪框架，结合边界框与传播掩码信息，在不依赖每序列调参的前提下，显著提升鲁棒性与泛化能力。在DanceTrack、MOT17、SoccerNet-tracking 2022和KITTI-tracking四个基准数据集上均取得性能提升，且优于现有掩码方法。代码将在录用后公开。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) involves identifying and consistently tracking objects across video sequences. Traditional tracking-by-detection methods, while effective, often require extensive tuning and lack generalizability. On the other hand, segmentation mask-based methods are more generic but struggle with tracking management, making them unsuitable for MOT. We propose a novel approach, McByte, which incorporates a temporally propagated segmentation mask as a strong association cue within a tracking-by-detection framework. By combining bounding box and propagated mask information, McByte enhances robustness and generalizability without per-sequence tuning. Evaluated on four benchmark datasets - DanceTrack, MOT17, SoccerNet-tracking 2022, and KITTI-tracking - McByte demonstrates performance gain in all cases examined. At the same time, it outperforms existing mask-based methods. Implementation code will be provided upon acceptance.
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