SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement

TL;DR

SurgAtt-Tracker is a novel framework for real-time surgical attention tracking that uses temporal proposal reranking and motion-aware refinement, supported by a large annotated dataset, to improve robustness and provide actionable FoV guidance.

Contribution

It introduces SurgAtt-Tracker, a new spatio-temporal learning approach for surgical attention tracking, and SurgAtt-1.16M, a large-scale benchmark dataset for training and evaluation.

Findings

Achieves state-of-the-art attention tracking performance.

Demonstrates robustness under occlusion and interference.

Provides effective frame-wise FoV guidance for robotic surgery.

Abstract

Accurate and stable field-of-view (FoV) guidance is critical for safe and efficient minimally invasive surgery, yet existing approaches often conflate visual attention estimation with downstream camera control or rely on direct object-centric assumptions. In this work, we formulate surgical attention tracking as a spatio-temporal learning problem and model surgeon focus as a dense attention heatmap, enabling continuous and interpretable frame-wise FoV guidance. We propose SurgAtt-Tracker, a holistic framework that robustly tracks surgical attention by exploiting temporal coherence through proposal-level reranking and motion-aware refinement, rather than direct regression. To support systematic training and evaluation, we introduce SurgAtt-1.16M, a large-scale benchmark with a clinically grounded annotation protocol that enables comprehensive heatmap-based attention analysis across procedures and institutions. Extensive experiments on multiple surgical datasets demonstrate that SurgAtt-Tracker consistently achieves state-of-the-art performance and strong robustness under occlusion, multi-instrument interference, and cross-domain settings. Beyond attention tracking, our approach provides a frame-wise FoV guidance signal that can directly support downstream robotic FoV planning and automatic camera control.