Kinematic structure correspondences via hypergraph matching

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Title: Kinematic structure correspondences via hypergraph matching
Author(s): Chang, HJ
Fischer, T
Petit, M
Zambelli, M
Demiris, Y
Item Type: Conference Paper
Abstract: In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.
Publication Date: 12-Dec-2016
Date of Acceptance: 2-Mar-2016
URI: http://hdl.handle.net/10044/1/30904
DOI: https://dx.doi.org/10.1109/CVPR.2016.457
ISSN: 1063-6919
Publisher: IEEE
Journal / Book Title: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 612139
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition
Publication Status: Published
Start Date: 2016-06-26
Finish Date: 2016-07-01
Conference Place: Las Vegas, USA
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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