Skeleton based Human Action Recognition using a Structured-Tree Neural Network

Muhammad Sajid Khan, Andrew Ware, Misha Karim, Nisar Bahoo and Muhammad Junaid Khalid
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Muhammad Sajid Khan: Computer Science Department, Army Public College of Management and Sciences, Khadim Hussain Road, Rawalpindi, Pakistan
Andrew Ware: Faculty of Computing, Engineering and Science, University of South Wales, United Kingdom
Misha Karim: Computer Science Department, Army Public College of Management and Sciences, Khadim Hussain Road, Rawalpindi, Pakistan
Nisar Bahoo: Computer Science Department, Army Public College of Management and Sciences, Khadim Hussain Road, Rawalpindi, Pakistan
Muhammad Junaid Khalid: Computer Science Department, Army Public College of Management and Sciences, Khadim Hussain Road, Rawalpindi, Pakistan

European Journal of Engineering and Technology Research, 2020, vol. 5, issue 8, 849-854

Abstract: The ability for automated technologies to correctly identify a human’s actions provides considerable scope for systems that make use of human-machine interaction. Thus, automatic3D Human Action Recognition is an area that has seen significant research effort. In work described here, a human’s everyday 3D actions recorded in the NTU RGB+D dataset are identified using a novel structured-tree neural network. The nodes of the tree represent the skeleton joints, with the spine joint being represented by the root. The connection between a child node and its parent is known as the incoming edge while the reciprocal connection is known as the outgoing edge. The uses of tree structure lead to a system that intuitively maps to human movements. The classifier uses the change in displacement of joints and change in the angles between incoming and outgoing edges as features for classification of the actions performed

Keywords: Structure-Tree Neural Network (STNN); Skeleton; Human Action Recognition (HAR) (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:5:y:2020:i:8:id:62004

DOI: 10.24018/ejeng.2020.5.8.2004

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