TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking

• URL: http://arxiv.org/abs/2508.07968v1
• Date: Mon, 11 Aug 2025 13:28:50 GMT
• Title: TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking
• Authors: Tony Danjun Wang, Christian Heiliger, Nassir Navab, Lennart Bastian,
• Abstract summary: We propose TrackOR, a framework for tackling long-term multi-person tracking and re-identification in the operating room.<n>TrackOR uses 3D geometric signatures to achieve state-of-the-art online tracking performance.<n>Our work shows that by leveraging 3D geometric information, persistent identity tracking becomes attainable.
• Score: 38.59801869721841
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations [60.51118188315758]
  Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment.<n>We identify three core cognitive challenges hindering this transition: spatial reasoning, long-horizon state tracking via mental simulation, and active exploration under partial observation.<n>We introduce CubeBench, a novel generative benchmark centered on the Rubik's Cube.
  arXiv  Detail & Related papers  (2025-12-29T09:25:56Z)
• RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics [53.053660003572965]
  We propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring.<n>RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning.<n>We present TraceSpatial-Bench, a challenging benchmark to evaluate spatial tracing.
  arXiv  Detail & Related papers  (2025-12-15T18:52:43Z)
• TrackStudio: An Integrated Toolkit for Markerless Tracking [0.0]
  There is a gap in accessible, integrated solutions that deliver sufficient tracking for non-experts across diverse settings.<n>TrackStudio was developed to address this gap by combining established open-source tools into a single, modular, GUI-based pipeline.<n>It provides automatic 2D and 3D tracking, calibration, preprocessing, feature extraction, and visualisation without requiring any programming skills.
  arXiv  Detail & Related papers  (2025-11-10T20:49:58Z)
• LiteTracker: Leveraging Temporal Causality for Accurate Low-latency Tissue Tracking [84.52765560227917]
  LiteTracker is a low-latency method for tissue tracking in endoscopic video streams.<n> LiteTracker builds on a state-of-the-art long-term point tracking method, and introduces a set of training-free runtime optimizations.
  arXiv  Detail & Related papers  (2025-04-14T05:53:57Z)
• Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room [36.67627283629717]
  We present a staff-centric modeling approach that characterizes individual team members through their distinctive movement patterns and physical characteristics.<n>We develop a generalizable re-identification framework that encodes sequences of 3D point clouds to capture shape and articulated motion patterns unique to each individual.<n>Our method achieves 86.19% accuracy on realistic clinical data while maintaining 75.27% accuracy when transferring between different environments.
  arXiv  Detail & Related papers  (2025-03-17T10:30:26Z)
• FeatureSORT: Essential Features for Effective Tracking [0.0]
  We introduce a novel tracker designed for online multiple object tracking with a focus on being simple, while being effective. By integrating distinct appearance features, including clothing color, style, and target direction, our tracker significantly enhances online tracking accuracy.
  arXiv  Detail & Related papers  (2024-07-05T04:37:39Z)
• QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds [51.05639500325598]
  We introduce QuadrupedGPT, designed to follow diverse commands with agility comparable to that of a pet.<n>Our agent shows proficiency in handling diverse tasks and intricate instructions, representing a significant step toward the development of versatile quadruped agents.
  arXiv  Detail & Related papers  (2024-06-24T12:14:24Z)
• PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search [64.28335667655129]
  Multiple object tracking is a critical task in autonomous driving. As tracking accuracy improves, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this paper, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy.
  arXiv  Detail & Related papers  (2024-03-23T04:18:49Z)
• The RPM3D project: 3D Kinematics for Remote Patient Monitoring [4.555816992996365]
  This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital).
  arXiv  Detail & Related papers  (2022-12-09T14:16:32Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Joint Scene and Object Tracking for Cost-Effective Augmented Reality Assisted Patient Positioning in Radiation Therapy [0.6299766708197884]
  The research done in the field of Augmented Reality (AR) for patient positioning in radiation therapy is scarce. We propose an efficient and cost-effective algorithm for tracking the scene and the patient to interactively assist the patient's positioning process.
  arXiv  Detail & Related papers  (2020-10-05T10:20:46Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.