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Type: Conference paper
Title: Efficient articulated trajectory reconstruction using dynamic programming and filters
Author: Valmadre, J.
Zhu, Y.
Sridharan, S.
Lucey, S.
Citation: Lecture Notes in Artificial Intelligence, 2012 / Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (ed./s), vol.7572 LNCS, iss.PART 1, pp.72-85
Publisher: Springer-Verlag
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science; 7572
ISBN: 9783642337178
ISSN: 0302-9743
Conference Name: European Conference on Computer Vision (ECCV) (7 Oct 2012 - 13 Oct 2012 : Florence, Italy)
Editor: Fitzgibbon, A.
Lazebnik, S.
Perona, P.
Sato, Y.
Schmid, C.
Statement of
Jack Valmadre, Yingying Zhu, Sridha Sridharan and Simon Lucey
Abstract: This paper considers the problem of reconstructing the motion of a 3D articulated tree from 2D point correspondences subject to some temporal prior. Hitherto, smooth motion has been encouraged using a trajectory basis, yielding a hard combinatorial problem with time complexity growing exponentially in the number of frames. Branch and bound strategies have previously attempted to curb this complexity whilst maintaining global optimality. However, they provide no guarantee of being more efficient than exhaustive search. Inspired by recent work which reconstructs general trajectories using compact high-pass filters, we develop a dynamic programming approach which scales linearly in the number of frames, leveraging the intrinsically local nature of filter interactions. Extension to affine projection enables reconstruction without estimating cameras.
Rights: © Springer-Verlag Berlin Heidelberg 2012
DOI: 10.1007/978-3-642-33718-5_6
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