用检测器闭环修正追踪记忆，提升喉部开口定位稳定性。
Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization
- 检测器闭环监督SAM2，通过置信度触发语义重置
- 在急诊插管视频中漂移率显著降低，漏检率下降37%
- 适合临床内镜视频追踪，尤其应对快速组织变形场景
喉部开口定位的时序稳定性因单帧检测器缺乏时间上下文、基础追踪器存在记忆漂移而难以保障。在急诊气管插管视频中，组织快速变形、遮挡及视觉模糊导致追踪误差累积。本文提出闭环记忆修正（CL-MC）框架，通过置信度对齐的状态决策，监督基于分割任意模型2（SAM2）的追踪器，并在高置信度检测时主动执行语义重置，覆盖被污染的追踪记忆。该方法无需训练即可有效缓解复杂内镜场景下的漂移积累。在真实急诊插管视频上，相比SAM2变体与开环方法，CL-MC显著降低漂移与漏检率。结果表明，记忆修正对可靠临床视频追踪至关重要。代码将公开于 https://github.com/huayuww/CL-MR。
原文摘要 · Abstract (English)
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.
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