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Controllable data sampling in the space of human poses
Author(s) -
Yang Kyungyong,
Youn Kibeom,
Lee Kyungho,
Lee Jehee
Publication year - 2015
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.1662
Subject(s) - computer science , exploit , sampling (signal processing) , artificial intelligence , space (punctuation) , machine learning , motion capture , computer vision , dimensionality reduction , curse of dimensionality , adaptive sampling , motion (physics) , domain (mathematical analysis) , mathematics , mathematical analysis , statistics , computer security , filter (signal processing) , monte carlo method , operating system
Markerless human pose recognition using a single‐depth camera plays an important role in interactive graphics applications and user interface design. Recent pose recognition algorithms have adopted machine learning techniques, utilizing a large collection of motion capture data. The effectiveness of the algorithms is greatly influenced by the diversity and variability of training data. We present a new sampling method that resamples a collection of human motion data to improve the pose variability and achieve an arbitrary size and level of density in the space of human poses. The space of human poses is high dimensional, and thus, brute‐force uniform sampling is intractable. We exploit dimensionality reduction and locally stratified sampling to generate either uniform or application specifically biased distributions in the space of human poses. Our algorithm learns to recognize such challenging poses as sitting, kneeling, stretching, and doing yoga using a remarkably small amount of training data. The recognition algorithm can also be steered to maximize its performance for a specific domain of human poses. We demonstrate that our algorithm performs much better than the Kinect software development kit for recognizing challenging acrobatic poses while performing comparably for easy upright standing poses. Copyright © 2015 John Wiley & Sons, Ltd.