SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking
  with Motion-Aware Memory
Authors: name: Cheng-Yen Yang, name: Hsiang-Wei Huang, name: Wenhao Chai, name: Zhongyu Jiang, name: Jenq-Neng Hwang

The Segment Anything Model 2 (SAM 2) has demonstrated strong performance in
object segmentation tasks but faces challenges in visual object tracking,
particularly when managing crowded scenes with fast-moving or self-occluding
objects. Furthermore, the fixed-window memory approach in the original model
does not consider the quality of memories selected to condition the image
features for the next frame, leading to error propagation in videos. This paper
introduces SAMURAI, an enhanced adaptation of SAM 2 specifically designed for
visual object tracking. By incorporating temporal motion cues with the proposed
motion-aware memory selection mechanism, SAMURAI effectively predicts object
motion and refines mask selection, achieving robust, accurate tracking without
the need for retraining or fine-tuning. SAMURAI operates in real-time and
demonstrates strong zero-shot performance across diverse benchmark datasets,
showcasing its ability to generalize without fine-tuning. In evaluations,
SAMURAI achieves significant improvements in success rate and precision over
existing trackers, with a 7.1% AUC gain on LaSOT_{ext} and a 3.5% AO
gain on GOT-10k. Moreover, it achieves competitive results compared to fully
supervised methods on LaSOT, underscoring its robustness in complex tracking
scenarios and its potential for real-world applications in dynamic
environments. Code and results are available at
https://github.com/yangchris11/samurai.