3732962
doi
10.5281/zenodo.3732962
oai:zenodo.org:3732962
user-collaborate_project
Sotiris Manitsaris
Centre for Robotics, MINES ParisTech, PSL Universite Paris
Alina Glushkova
Centre for Robotics, MINES ParisTech, PSL Universite Paris
Towards a Professional Gesture Recognition with RGB-D from Smartphone
Pablo Vicente Monivar
Centre for Robotics, MINES ParisTech, PSL Universite Paris
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
pose estimation
smartphone
Hidden Markov Models
gesture recognition
depth map
<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
3732961
user-collaborate_project
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;
1585686013.946932
2534940
md5:f8e0f320f11ed9fd04d0f045a73d007a
https://zenodo.org/records/3732962/files/ICVS_Paper VFinal.pdf
public
10.5281/zenodo.3732961
isVersionOf
doi