TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking

TL;DR

TrackOR advances long-term multi-person tracking in operating rooms by leveraging 3D geometric signatures, enabling personalized, staff-centric analysis and support for surgical teams.

Contribution

It introduces a novel 3D geometric signature-based framework for robust online and offline multi-person tracking in surgical environments, improving accuracy and enabling personalized insights.

Findings

Achieved +11% association accuracy over baseline.

Enabled offline recovery for analysis-ready trajectories.

Facilitated staff-centric, personalized surgical support.

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.