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Highly articulated kinematic structure estimation combining motion and skeleton information

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Title: Highly articulated kinematic structure estimation combining motion and skeleton information
Authors: Chang, HJ
Demiris, Y
Item Type: Journal Article
Abstract: In this paper, we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view 2D image sequence. In contrast to prior motion-based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology via a successive iterative merging strategy. The iterative merge process is guided by a density weighted skeleton map which is generated from a novel object boundary generation method from sparse 2D feature points. Our main contributions can be summarised as follows: (i) An unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information. (ii) An iterative fine-to-coarse merging strategy for adaptive motion segmentation and structural topology embedding. (iii) A skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method. (iv) A new highly articulated object dataset with ground truth annotation. We have verified the effectiveness of our proposed method in terms of computational time and estimation accuracy through rigorous experiments. Our experiments show that the proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.
Issue Date: 1-Sep-2017
Date of Acceptance: 12-Aug-2017
URI: http://hdl.handle.net/10044/1/50417
DOI: 10.1109/TPAMI.2017.2748579
ISSN: 0162-8828
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2165
End Page: 2179
Journal / Book Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 40
Issue: 9
Copyright Statement: © 2017 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
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Highly articulated kinematic structure estimation
adaptive motion segmentation
density weighted silhouette generation from sparse points
adaptive kernel selection
Biomechanical Phenomena
Databases, Factual
Image Processing, Computer-Assisted
Pattern Recognition, Automated
Video Recording
Image Processing, Computer-Assisted
Video Recording
Databases, Factual
Pattern Recognition, Automated
Biomechanical Phenomena
0801 Artificial Intelligence and Image Processing
0806 Information Systems
0906 Electrical and Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status: Published
Online Publication Date: 2017-09-03
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering