cs.AI updates on arXiv.org 10月03日
VOS模型在腹腔镜胆囊切除视频中的点追踪失败分析
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本文系统分析了点追踪在腹腔镜胆囊切除视频中的失败模式,对比了点追踪与分割掩码初始化的性能,并提出了改进建议。

arXiv:2510.02100v1 Announce Type: cross Abstract: Video object segmentation (VOS) models such as SAM2 offer promising zero-shot tracking capabilities for surgical videos using minimal user input. Among the available input types, point-based tracking offers an efficient and low-cost alternative, yet its reliability and failure cases in complex surgical environments are not well understood. In this work, we systematically analyze the failure modes of point-based tracking in laparoscopic cholecystectomy videos. Focusing on three surgical targets, the gallbladder, grasper, and L-hook electrocautery, we compare the performance of point-based tracking with segmentation mask initialization. Our results show that point-based tracking is competitive for surgical tools but consistently underperforms for anatomical targets, where tissue similarity and ambiguous boundaries lead to failure. Through qualitative analysis, we reveal key factors influencing tracking outcomes and provide several actionable recommendations for selecting and placing tracking points to improve performance in surgical video analysis.

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视频对象分割 点追踪 腹腔镜胆囊切除 失败分析 性能优化
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