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.

翻译：过去一年中，多个基于Segment Anything Model 2（SAM2）扩展的目标跟踪流程被提出，其方法是通过用户在初始化帧上提供的单一示例模板来跟踪并分割目标。我们建议在专门设计用于挑战跟踪方法的快速运动目标（FMO）数据集上对这些高性能跟踪器（SAM2、EfficientTAM、DAM4SAM和SAMURAI）进行基准测试。目标是通过提供这些跟踪器行为的更详细洞察，更好地理解当前最先进跟踪器的局限性。我们表明，总体而言，跟踪器DAM4SAM和SAMURAI在更具挑战性的序列上表现良好。