Instrument Tracking with Rigid Part Mixtures Model

Tracking instruments in video-assisted minimally invasive surgeries is an attractive and open computer vision problem. A tracker successfully locating instruments would immediately find applications in manual and robotic interventions in the operating theater. We describe a tracking method that uses a rigidly structured model of instrument parts. The rigidly composed parts encode diverse, pose-specific appearance mixtures of the tool. This rigid part mixtures model then jointly explains the evolving structure of the tool parts by switching between mixture components during tracking. We evaluate our approach on publicly available datasets of in-vivo sequences and demonstrate state-of-the-art results.

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