TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking
This work addresses the need for consistent staff tracking in surgical environments to improve team efficiency and safety, representing an incremental advance in domain-specific tracking methods.
The paper tackles the problem of long-term multi-person tracking and re-identification in operating rooms to enable personalized intelligent support for surgical teams, achieving state-of-the-art online tracking performance with an 11% improvement in Association Accuracy over the strongest baseline.
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.