Abstract
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.
Abstract (translated)
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领域，长期目标是改善患者结果。为了所有工作人员开发个性化智能需要保持长时间手术过程中每个人员的位置状态，这仍然面临众多计算挑战。我们提出了TrackOR框架，用于解决手术室内的长期多人跟踪和重识别问题。TrackOR利用3D几何签名实现了最先进的在线跟踪性能（比最强基线提高了+11%的关联准确性），同时还能实现有效的离线恢复过程以创建可用于分析的轨迹。 我们的工作表明，通过利用三维几何信息，持续身份跟踪变得可行，这使得向更细致、以工作人员为中心的分析转变成为可能，这对于手术室内的个性化智能系统至关重要。这种新能力开启了许多应用，包括我们提出的时序路径印记，该技术可以将原始跟踪数据转化为提高团队效率和安全性的可操作见解，并最终提供个性化的支持。
URL
https://arxiv.org/abs/2508.07968
