在快速移动物体数据集上评测SAM2系追踪器性能表现
Benchmarking SAM2-based Trackers on FMOX
- 基于SAM2构建的四种追踪器在高速运动场景下对比测试
- DAM4SAM与SAMURAI在挑战性序列中表现更优
- 为理解先进追踪器局限性提供新视角，适合追踪研究者参考
过去一年中，多种基于分割一切模型2（SAM2）的物体追踪流程被提出，其方法是从用户提供的初始帧单模板中跟踪并分割目标。我们提议在专为挑战追踪方法而设计的包含快速移动物体（FMO）的数据集上对这些高性能追踪器（SAM2、EfficientTAM、DAM4SAM和SAMURAI）进行基准测试。目标是通过更深入的分析，更好地理解当前顶尖追踪器的局限性。结果显示，在更具挑战性的序列中，DAM4SAM和SAMURAI整体表现更佳。
原文摘要 · Abstract (English)
Several object tracking pipelines extending Segment Anything Model 2 (SAM2) have been proposed in the past year, where the approach is to follow and segment the object from a single exemplar template provided by the user on a initialization frame. We propose to benchmark these high performing trackers (SAM2, EfficientTAM, DAM4SAM and SAMURAI) on datasets containing fast moving objects (FMO) specifically designed to be challenging for tracking approaches. The goal is to understand better current limitations in state-of-the-art trackers by providing more detailed insights on the behavior of these trackers. We show that overall the trackers DAM4SAM and SAMURAI perform well on more challenging sequences.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。