Segment Anything Meets Point Tracking
Authors: name: Frano Rajič, name: Lei Ke, name: Yu-Wing Tai, name: Chi-Keung Tang, name: Martin Danelljan, name: Fisher Yu

The Segment Anything Model (SAM) has established itself as a powerful
zero-shot image segmentation model, employing interactive prompts such as
points to generate masks. This paper presents SAM-PT, a method extending SAM's
capability to tracking and segmenting anything in dynamic videos. SAM-PT
leverages robust and sparse point selection and propagation techniques for mask
generation, demonstrating that a SAM-based segmentation tracker can yield
strong zero-shot performance across popular video object segmentation
benchmarks, including DAVIS, YouTube-VOS, and MOSE. Compared to traditional
object-centric mask propagation strategies, we uniquely use point propagation
to exploit local structure information that is agnostic to object semantics. We
highlight the merits of point-based tracking through direct evaluation on the
zero-shot open-world Unidentified Video Objects (UVO) benchmark. To further
enhance our approach, we utilize K-Medoids clustering for point initialization
and track both positive and negative points to clearly distinguish the target
object. We also employ multiple mask decoding passes for mask refinement and
devise a point re-initialization strategy to improve tracking accuracy. Our
code integrates different point trackers and video segmentation benchmarks and
will be released at https://github.com/SysCV/sam-pt.