Statistical Shape Learning for 3D Tracking
Tannenbaum, Allen R.
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In this note, we consider the use of 3D models for visual tracking in controlled active vision. The models are used for a joint 2D segmentation/3D pose estimation procedure in which we automatically couple the two processes under one energy functional. Further, employing principal component analysis from statistical learning, can train our tracker on a catalog of 3D shapes, giving a priori shape information. The segmentation itself is information-based. This allows us to track in uncertain adversarial environments. Our methodology is demonstrated on some real sequences which illustrate its robustness on challenging scenarios.