SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking
  with Motion-Aware Memory
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.
673d668e7c9e8932533502b8
Cheng-Yen Yang
673d668e7c9e8932533502b9
Hsiang-Wei Huang
673d668e7c9e8932533502ba
Wenhao Chai
claimed_verified
2024-11-20T10:08:27.233Z
637c7503fe115289cfecbe6b
https://cdn-avatars.huggingface.co/v1/production/uploads/1676361945047-637c7503fe115289cfecbe6b.jpeg
Wenhao Chai
user
wchai
673d668e7c9e8932533502bb
Zhongyu Jiang
claimed_verified
2024-11-21T08:09:49.008Z
65714e2a6bbfca64615f9931
/avatars/9048b08655a7b0a877a47c7424abe50e.svg
Zhongyu Jiang
user
Nitre
673d668e7c9e8932533502bc
Jenq-Neng Hwang
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking
  with Motion-Aware Memory
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.
SAMURAI enhances the Segment Anything Model 2 for visual object tracking by integrating motion cues and a memory selection mechanism, improving performance and achieving real-time zero-shot tracking without retraining.
object segmentation visual object tracking crowded scenes fast-moving objects self-occluding objects fixed-window memory image features motion-aware memory selection mask selection real-time tracking zero-shot performance benchmark datasets success rate precision AUC AO LaSOT_ext GOT-10k competitive results