Unsupervised Object Pose Classification from Short Video Sequences
MICHIGAN UNIV ANN ARBOR DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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We address the problem of recognizing the pose of an object category from video sequences capturing the object under small camera movements. This scenario is relevant in applications such as robotic object manipulation or autonomous navigation. We introduce a new algorithm where we model an object category as a collection of non parametric probability densities capturing appearance and geometrical variability within a small area of the viewing sphere for different object instances. By regarding the set of frames of the video as realizations of such probability densities, we cast the problem of object pose classification as the one of matching i.e., comparing information divergence of probably density functions in testing and training. Our work can be also related to statistical manifold learning. By performing dimensionality reduction on the manifold of learned PDFs, we show that the embedding in the 3D Euclidean space yield meaningful trajectories which can be parameterized by the pose coordinates on the viewing sphere, this enables an unsupervised learning procedure for pose classification. Our experimental results on both synthesized and real world data show promising results toward the goal of accurate and efficient pose classification of object categories from video sequences.
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