用3D几何信息实现手术室长期多人跟踪，提升智能支持精度
TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking
- 利用3D几何特征实现持续身份追踪
- 在线跟踪准确率比最强基线高11%
- 适合需要个性化手术团队分析的研究者
为外科团队提供智能支持是自动化手术场景理解的关键前沿，长期目标是改善患者预后。为所有医护人员构建个性化智能系统，需在长时间手术中持续追踪人员位置与身份，这仍面临诸多计算挑战。我们提出TrackOR框架，用于解决手术室内的长期多人群体跟踪与重识别问题。TrackOR通过3D几何签名实现最先进的在线跟踪性能（相比最强基线提升11%的关联准确率），同时支持有效的离线轨迹恢复，生成可分析的轨迹数据。研究表明，利用3D几何信息可实现持久的身份追踪，推动向更细粒度、以人员为中心的分析转变，从而支持个性化智能系统。该能力催生多种应用，包括我们提出的时序路径印记，将原始追踪数据转化为提升团队效率与安全的可操作洞察，最终实现个性化支持。
原文摘要 · Abstract (English)
Providing intelligent support to surgical teams is a key frontier in automated surgical scene understanding, with the long-term goal of improving patient outcomes. Developing personalized intelligence for all staff members requires maintaining a consistent state of who is located where for long surgical procedures, which still poses numerous computational challenges. We propose TrackOR, a framework for tackling long-term multi-person tracking and re-identification in the operating room. TrackOR uses 3D geometric signatures to achieve state-of-the-art online tracking performance (+11% Association Accuracy over the strongest baseline), while also enabling an effective offline recovery process to create analysis-ready trajectories. Our work shows that by leveraging 3D geometric information, persistent identity tracking becomes attainable, enabling a critical shift towards the more granular, staff-centric analyses required for personalized intelligent systems in the operating room. This new capability opens up various applications, including our proposed temporal pathway imprints that translate raw tracking data into actionable insights for improving team efficiency and safety and ultimately providing personalized support.
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