  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.

Computer Vision and Pattern Recognition