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Articulated pose identification with sparse point features
journal contribution
posted on 2016-02-09, 12:42 authored by Baihua LiBaihua Li, Qinggang MengQinggang Meng, Horst HolsteinWe propose a general algorithm for identifying an arbitrary pose of an articulated subject with sparse point features. The algorithm aims to identify a one-to-one correspondence between a model point-set and an observed point-set taken from freeform motion of the articulated subject. We avoid common assumptions such as pose similarity or small motions with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion data demonstrate the ability of the algorithm to perform the identification task. Reliability is increasingly compromised with increasing data noise and segmental distortion, but the algorithm can tolerate moderate levels. This work contributes to establishing a crucial self-initializing identification in model-based point-feature tracking for articulated motion.
History
School
- Science
Department
- Computer Science
Published in
IEEE Transactions on Systems, Man, and Cybernetics, Part B: CyberneticsVolume
34Issue
3Pages
1412 - 1422Citation
LI, B., MENG, Q. and HOLSTEIN, H., 2004. Articulated pose identification with sparse point features. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34 (3), pp.1412-1422Publisher
© IEEEVersion
- NA (Not Applicable or Unknown)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2004Notes
This paper is closed access.ISSN
1083-4419Publisher version
Language
- en