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. The allows us to track in uncertain adversarial environments. Our methodology is demonstrated on some real sequences which illustrate its robustness on challenging scenarios.
['Romeil Sandhu', 'Shawn Lankton', 'Samuel Dambreville', 'Allen Tannenbaum']
Statistical shape learning for 3D tracking