Abstract
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.
Abstract (translated)
在过去的一年中，提出了几种基于Segment Anything Model 2（SAM2）的多目标跟踪流水线方法，这些方法旨在通过用户在初始化帧上提供的单个示例模板来追踪和分割对象。我们提议对包含快速移动物体（FMO）的数据集进行基准测试，该数据集专门设计用于挑战现有的跟踪方法。我们的目标是通过提供更详细的关于这些跟踪器行为的见解，更好地理解当前最先进的跟踪器的局限性。结果显示，在更具挑战性的序列上，DAM4SAM和SAMURAI这两款追踪器整体表现较好。
URL
https://arxiv.org/abs/2512.09633
