This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to select multiple objects in videos for tracking, corresponding to their specific requirements. These interaction methods comprise click, stroke, and text, each possessing unique benefits and capable of being employed in combination. As a result, SAM-Track can be used across an array of fields, ranging from drone technology, autonomous driving, medical imaging, augmented reality, to biological analysis. SAM-Track amalgamates Segment Anything Model (SAM), an interactive key-frame segmentation model, with our proposed AOT-based tracking model (DeAOT), which secured 1st place in four tracks of the VOT 2022 challenge, to facilitate object tracking in video. In addition, SAM-Track incorporates Grounding-DINO, which enables the framework to support text-based interaction. We have demonstrated the remarkable capabilities of SAM-Track on DAVIS-2016 Val (92.0%), DAVIS-2017 Test (79.2%)and its practicability in diverse applications. The project page is available at: https://github.com/z-x-yang/Segment-and-Track-Anything.

翻译：本报告提出一个名为“分割并追踪一切”（Segment And Track Anything，SAM-Track）的框架，使用户能够精确且高效地分割并追踪视频中的任意物体。此外，SAM-Track采用多模态交互方法，用户可根据具体需求选择视频中的多个物体进行追踪。这些交互方式包括点击、笔画和文本，每种方式均有独特优势并可组合使用。因此，SAM-Track可应用于无人机技术、自动驾驶、医学影像、增强现实及生物分析等多个领域。SAM-Track结合了交互式关键帧分割模型Segment Anything Model（SAM）与我们提出的基于AOT的追踪模型（DeAOT，在VOT 2022挑战赛四个赛道上均获得第一名），以实现视频中的物体追踪。同时，SAM-Track集成了Grounding-DINO，使框架支持基于文本的交互。我们在DAVIS-2016验证集（92.0%）、DAVIS-2017测试集（79.2%）上展示了SAM-Track的卓越性能，并验证了其在不同应用场景中的实用性。项目页面地址：https://github.com/z-x-yang/Segment-and-Track-Anything。