We propose a novel Python-based Human Activity Pose Tracking data processing framework (PyHAPT). It provides the functionality to efficiently process annotated human pose tracking raw video data collected in unconstrained environments. Besides, PyHAPT provides the functionalities of interpolation to recover the missing joints data and data visualization that gives insights into the spatial–temporal skeletal information. The processed data could be readily used for developing new human activity recognition deep learning models, which could be deployed on mobile service robots.

PyHAPT: A Python-based Human Activity Pose Tracking data processing framework

Quan, Hao;Bonarini, Andrea
2022-01-01

Abstract

We propose a novel Python-based Human Activity Pose Tracking data processing framework (PyHAPT). It provides the functionality to efficiently process annotated human pose tracking raw video data collected in unconstrained environments. Besides, PyHAPT provides the functionalities of interpolation to recover the missing joints data and data visualization that gives insights into the spatial–temporal skeletal information. The processed data could be readily used for developing new human activity recognition deep learning models, which could be deployed on mobile service robots.
2022
Robotics
Data processing
Data visualization
Human activity recognition
Deep learning
File in questo prodotto:
File Dimensione Formato  
SoftwareImpacts1-s2.0-S2665963822000446-main.pdf

accesso aperto

Descrizione: Main text
: Publisher’s version
Dimensione 541.64 kB
Formato Adobe PDF
541.64 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1218987
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact