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

--> Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20636 [Submitted on 24 Feb 2026] Title: SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement Authors: Rulin Zhou , Guankun Wang , An Wang , Yujie Ma , Lixin Ouyang , Bolin Cui , Junyan Li , Chaowei Zhu , Mingyang Li , Ming Chen , Xiaopin Zhong , Peng Lu , Jiankun Wang , Xianming Liu , Hongliang Ren View a PDF of the paper titled SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement, by Rulin Zhou and 14 other authors View PDF HTML Abstract: Accurate and stable field-of-view 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. Subjects: Computer Vision and Pattern Recogn...