publication . Conference object . 2017

Using multi-dimensional correlation for matching and alignment of MoCap and video signals

Michele Buccoli; Bruno Di Giorgi; Massimiliano Zanoni; Fabio Antonacci; Augusto Sarti;
Open Access
  • Published: 30 Nov 2017
  • Publisher: IEEE
Motion analysis and tracking often relies on multimodal signals, e.g., video, depth map, motion capture (MoCap), due to the completeness of information they jointly provide. The joint analysis of multimodal signals requires to know the correct timing, i.e., the signals to be aligned. In this paper we propose an approach to automatically estimate the correct matching and alignment between a video and a MoCap recording acquired from the same session, based on the multi-dimensional correlation of velocity-based features extracted from the two recordings. We validate our approach over a dataset of dance recordings of four genres, and we achieve promising results for...
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ACM Computing Classification System: ComputingMethodologies_COMPUTERGRAPHICS
free text keywords: Correlation, Feature extraction, Reliability, Streaming media, Cameras, Three-dimensional displays, Motion capture, Computer vision, Depth map, Artificial intelligence, business.industry, business, Computer science, Completeness (statistics), Motion analysis, Joint analysis, Multi dimensional, Correlation, Feature extraction
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Funded by
EC| WhoLoDancE
Whole-Body Interaction Learning for Dance Education
  • Funder: European Commission (EC)
  • Project Code: 688865
  • Funding stream: H2020 | RIA
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Conference object . 2017
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Conference object . 2017
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