Relational databases for motion data. (2016)

• Record Type: Journal Article
• Authors: Riaz, Qaiser; KrÁ¼ger, Björn; Weber, Andreas
• Is Part Of: International journal of innovative computing and applications. Volume 7:Number 3(2016)
• Page Start: 119
• Page End: 134
• Publication Date: 2016
• Subjects: mocap -- relational databases -- motion capture -- motion data -- data capture -- relational database management systems -- RDBMS -- sensor modalities -- semantic annotations -- motion sequences; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Motion capture data have been widely used in applications ranging from video games and animations to simulations and virtual environments. Moreover, all data-driven approaches for analysis and synthesis of motions are depending on motion capture data. Although multiple large motion capture datasets are freely available for research, there is no system which can provide a centralised access to all of them in an organised manner. In this paper, we show that using a relational database management system (RDBMS) to store data not only provide such a centralised access to the data, but also allows to include other sensor modalities (e.g., accelerometer data) and various semantic annotations. We present two applications for our system: a motion capture player where motions sequences can be retrieved from large datasets using SQL queries and the automatic construction of statistical models which can further be used for complex motion analysis and motions synthesis tasks.