SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware check out this SAM2 vs SAMURAI comparison! - paper: - code: - license: Apache-2.0

- enhance the visual tracking accuracy of SAM 2 by incorporating motion information through motion modeling, to effectively handle the fast-moving and occluded objects - propose a motion-aware memory selection mechanism that reduces error in crowded scenes in contrast to the original fixed-window memory by selectively storing relevant frames decided by a mixture of motion and affinity scores

state-of-the-art performance on various VOT benchmarks, including GOT-10k, LaSOT-ext, and NeedForSpeed

can't wait to have some fun with SAMURAI as I did with SAM2

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

The researchers aim to enhance the visual object tracking capabilities of the Segment Anything Model 2 (SAM 2) by addressing its limitations in handling crowded scenes and managing occlusions. The proposed SAMURAI framework demonstrates significant improvements over existing methods on various visual object tracking benchmarks, such as LaSOT, LaSOT ext, and GOT-10k, without the need for additional training or fine-tuning. full paper:

Anyone who is against ML/AI tools should be locked in a room and forced to rotoscope this mask by hand. They will be e/acc when they are let out.

Amazing work ! 😍

This is insane, great work !!

Absolutely mind blowing! IDK how you keep improving so quickly?? Any experiments with these results on live video feeds?

This is very cool! Tracking is incredibly hard. Would love to see this applied to multi-object tracking

I fear this kind of instruments among others are gonna be used in drones in their last mile before chasing the running target