Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization

Huayu Wang, Bahaa Alattar, Cheng-Yen Yang, Hsiang-Wei Huang, Jung Heon Kim, Linda Shapiro, Nathan J White, Jenq-Neng Hwang

Keywords: Video Object Detection, Video Laryngoscopy, YOLO, SAM2;

Abstract: Temporal stability in glottic opening localization remains challenging due to the complementary weaknesses of single-frame detectors and foundation-model trackers: the former lacks temporal context, while the latter suffers from memory drift. Specifically, in video laryngoscopy, rapid tissue deformation, occlusions, and visual ambiguities in emergency settings require a robust, temporally aware solution that can prevent progressive tracking errors. We propose Closed-Loop Memory Correction (CL-MC), a detector-in-the-loop framework that supervises SAM2 through confidence-aligned state decisions and active memory rectification. High-confidence detections trigger semantic resets that overwrite corrupted tracker memory, enabling fully training-free, drift-free tracking in complex endoscopic scenes. On emergency intubation videos, CL-MC achieves state-of-the-art performance, significantly reducing drift and missing rate compared with the SAM2 variants and open loop based methods. Our results establish memory correction as a crucial component for reliable clinical video tracking.

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Application: Endoscopy

Registration Requirement: Yes

Reproducibility: https://github.com/huayuww/CL-MR

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 352