5658483
doi
10.1007/978-3-030-34995-0_22
oai:zenodo.org:5658483
user-mingei-h2020
user-collaborate_project
user-eu
Manitsaris, Sotiris
Centre for Robotics, MINES ParisTech, PSL Université
Glushkova, Alina
Centre for Robotics, MINES ParisTech, PSL Université
Towards a Professional Gesture Recognition with RGB-D from Smartphone
Monivar, Pablo Vicente
Centre for Robotics, MINES ParisTech, PSL Université
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Abstract. The goal of this work is to build the basis for a smartphone application that provides functionalities for recording human motion data, train machine learning algorithms and recognize professional gestures. First, we take advantage of the new mobile phone cameras, either infrared or stereoscopic, to record RGB-D data. Then, a bottom-up pose estimation algorithm based on Deep Learning extracts the 2D human skeleton and exports the 3rd dimension using the depth. Finally, we use a gesture recognition engine, which is based on K-means and Hidden Markov Models (HMMs). The performance of the machine learning algorithm has been tested with professional gestures using a silk-weaving and a TV-assembly datasets.</p>
Zenodo
2020-03-30
info:eu-repo/semantics/conferencePaper
5658482
user-mingei-h2020
user-collaborate_project
user-eu
award_title=Representation and Preservation of Heritage Crafts; award_number=822336; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/822336; funder_id=00k4n6c32; funder_name=European Commission;
award_title=Co-production CeLL performing Human-Robot Collaborative AssEmbly; award_number=820767; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/820767; funder_id=00k4n6c32; funder_name=European Commission;
1636906698.576579
2534940
md5:f8e0f320f11ed9fd04d0f045a73d007a
https://zenodo.org/records/5658483/files/ICVS_Paper VFinal.pdf
public