SAMURAI vs. MetaAI's SAM 2! Traditional visual object tracking struggles in crowded, fast-moving, or self-occluded scenes, as does SAM2. Meet SAMURAI: a completely open-source adaptation of the Segment Anything Model for zero-shot visual tracking! Here's why it's a game-changer: 🚫 No need for retraining or finetuning 🎯 Boosts success... rate and precision 🤖 Motion-aware memory selection 💪 Zero-shot performance on diverse datasets But that's not all: 🔬 Refines mask selection 🔮 Predicts object motion effectively 📈 Gains: 7.1% AUC on LaSOT, 3.5% AO on GOT-10k 🏆 Competes with fully supervised methods without extra training Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights & tutorials on AI and Machine Learning.show more

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The paper focuses on adapting the Segment Anything Model 2 (SAM 2) for visual object tracking, which is a challenging task for the original model. SAM 2 has shown strong performance in object segmentation, but it faces difficulties in handling crowded scenes with fast-moving or self-occluding objects. The improvements in tracking accuracy are attributed to the incorporation of motion information and the enhanced memory selection mechanism. These advancements help SAMURAI better handle challenging scenarios, such as crowded scenes and occlusions, where the original SAM 2 model struggles. full paper:

Very cool! Isn’t SAM 2 open source too?

Perhaps combination of different colour models can fetch promising results. Seems this is only on RGB, as in when smokes covers Samurai fails to capture the subject.

Accuracy is so crazy

Awesome simulation

Great choice of the video to test it! Loved it!

Now this is what AI should be used for, not generative AI that is using resources without any reason other than a lack of care to learn to make things the human way that gives things meaning .

I’m curious if you understand how ai tracking works for technologies like hudle and veo for basketball?