解决密集场景下追踪的掩码错误与身份切换问题
SAMIDARE: Advanced Tracking-by-Segmentation for Dense Scenarios
- 通过密度感知掩码重生成和选择性记忆更新，动态控制目标特征
- 在SportsMOT上提升2.5点HOTA和4.2点IDF1，优于基线
- 适合需要高精度密集多目标追踪的体育视频分析场景
自动体育分析要求鲁棒的多目标跟踪（MOT），但基于分割的方法在密集场景中常出现掩码错误和身份切换。我们提出SAMIDARE框架，通过三个关键组件增强SAM2MOT在拥挤场景中的表现：(1) 密度感知掩码重生成，(2) 选择性记忆更新，二者共同实现自适应掩码控制以保持目标特征完整性；(3) 状态感知关联与新轨迹初始化，提升在相互遮挡和频繁帧丢失下的鲁棒性。在SportsMOT数据集上的评估显示，SAMIDARE达到领先性能，在验证集上较基线提升2.5点HOTA和4.2点IDF1。结果表明，通过掩码控制与状态感知关联实现的自适应特征管理，为密集体育追踪提供了高效可靠的解决方案。代码已公开于https://github.com/ZabuZabuZabu/SAMIDARE。
原文摘要 · Abstract (English)
Automated sports analysis demands robust multi-object tracking (MOT), yet segmentation-based methods often struggle with mask errors and ID switches in dense scenes. We propose SAMIDARE, a framework that enhances SAM2MOT for crowded scenes through three key components: (1) density-aware mask re-generation and (2) selective memory updates, both for adaptive mask control to preserve target feature integrity, and (3) state-aware association and new track initialization, which improves robustness under mutual occlusions and frequent frame-out events. Evaluated on the SportsMOT dataset, SAMIDARE achieves state-of-the-art performance, outperforming the baseline by 2.5 HOTA and 4.2 IDF1 points on the validation set. These results demonstrate that adaptive feature management using mask control and state-aware association provide a robust and efficient solution for dense sports tracking. Code is available at https://github.com/ZabuZabuZabu/SAMIDARE
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。