Open AccessAR Oriented Pose Matching Mechanism from Motion Capture Data
Author(s) -
Javid Iqbal,
Manjit Singh Sidhu,
Mutahir Bin Mohamed Ariff
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.35.22749
Subject(s) - artificial intelligence , computer science , computer vision , matching (statistics) , frame (networking) , dance , key frame , pose , trainer , motion (physics) , feature matching , motion capture , feature (linguistics) , feature extraction , pattern recognition (psychology) , mathematics , art , telecommunications , linguistics , statistics , philosophy , literature , programming language
Pose matching and skeletal mapping method are an integral part of Augmented Reality (AR) based learning technology. In this paper a mechanism for pose matching is presented based on extraction of skeletal data from the dance trainer’s physical movements in the form of color defined images snapped by Kinect, where each pose is modelled by a sequence of key movements and continues data frames. In order to extract the exact matched pose, the frame sequence is divided into pose feature frame and skeletal data frame by the use of pose matching dance training movement recognition algorithm (PMDTMR). This proposed algorithm is compared with other published methods in terms of frame level accuracy and learning time of dance session. The experimental results show that the proposed algorithm outperforms the state of art techniques for successful identification and recognition of matched pose between the dance trainer and the expert of the pre-recorded video through the Kinect sensor.