Paper: https://arxiv.org/pdf/2411.11922)
Github: https://github.com/yangchris11/samurai)
Blog: https://yangchris11.github.io/samurai/)
The paper introduces SAMURAI, a novel visual object tracking method that enhances the Segment Anything Model 2 (SAM 2) for improved accuracy and robustness. SAMURAI addresses SAM 2's limitations in handling crowded scenes and occlusions by incorporating motion cues and a motion-aware memory selection mechanism. This allows SAMURAI to accurately track objects in real-time, even with rapid movement or self-occlusion, without requiring retraining. The method achieves state-of-the-art performance on various benchmarks, demonstrating its effectiveness and generalization capabilities. Code and results are publicly available.
ai , computer vision , cv , university of washington , artificial intelligence , arxiv , research , paper , publication
