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KeyFrame extraction for human motion capture data via multiple binomial fitting
Author(s) -
Xu Chenxu,
Yu Wenjie,
Li Yanran,
Lu Xuequan,
Wang Meili,
Yang Xiaosong
Publication year - 2020
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1976
Subject(s) - computer science , redundancy (engineering) , motion (physics) , sequence (biology) , binomial (polynomial) , artificial intelligence , motion estimation , cluster analysis , algorithm , computer vision , mathematics , statistics , genetics , biology , operating system
In this paper, we make two contributions. The first is to propose a new keyframe extraction algorithm, which reduces the keyframe redundancy and reduces the motion sequence reconstruction error. Secondly, a new motion sequence reconstruction method is proposed, which further reduces the error of motion sequence reconstruction. Specifically, we treated the input motion sequence as curves, then the binomial fitting was extended to obtain the points where the slope changes dramatically in the vicinity. Then we took these points as inputs to obtain keyframes by density clustering. Finally, the motion curves were segmented by keyframes and the segmented curves were fitted by binomial formula again to obtain the binomial parameters for motion reconstruction. Experiments show that our methods outperform existing techniques, in terms of reconstruction error.