TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking

2025-08-11

AI Summary

Persistent, long-term, multi-person identity tracking in operating rooms remains challenging, hindering personalized intelligent intraoperative assistance. Method: This paper proposes an online-offline collaborative tracking framework integrating 3D geometric features: the online stage employs geometric signatures for robust multi-object association; the offline stage introduces trajectory recovery and temporal path imprinting to enhance identity consistency. Contribution/Results: To our knowledge, this is the first work to achieve high-accuracy (11% improvement in association accuracy), long-duration, and interpretable identity trajectory reconstruction in real surgical environments. The resulting complete individual motion trajectories enable team behavioral modeling and fine-grained assessment of procedural efficiency and safety. Moreover, they establish a foundational basis for personalized intraoperative interventions.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Achieving long-term multi-person tracking in operating rooms
• Enhancing surgical team efficiency through personalized intelligence
• Improving tracking accuracy with 3D geometric signatures

Innovation

• 3D geometric signatures for tracking
• Online and offline trajectory recovery
• Temporal pathway imprints for insights