Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918350200766464 |
|---|---|
| author | Wang, Huayu Alattar, Bahaa Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Jung Heon Shapiro, Linda White, Nathan Hwang, Jenq-Neng |
| author_facet | Wang, Huayu Alattar, Bahaa Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Jung Heon Shapiro, Linda White, Nathan Hwang, Jenq-Neng |
| contents | 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 Segment Anything Model 2(SAM2) through confidence-aligned state decisions and active memory rectification. High-confidence detections trigger semantic resets that overwrite corrupted tracker memory, effectively mitigating drift accumulation with a training-free foundation tracker 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. Our code will be available in https://github.com/huayuww/CL-MR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization Wang, Huayu Alattar, Bahaa Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Jung Heon Shapiro, Linda White, Nathan Hwang, Jenq-Neng Computer Vision and Pattern Recognition 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 Segment Anything Model 2(SAM2) through confidence-aligned state decisions and active memory rectification. High-confidence detections trigger semantic resets that overwrite corrupted tracker memory, effectively mitigating drift accumulation with a training-free foundation tracker 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. Our code will be available in https://github.com/huayuww/CL-MR. |
| title | Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.19380 |