Implementation code for the Annotation Tool that is used for LARa dataset, presented in the Journal "LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes", see https://www.mdpi.com/1424-8220/20/15/4083
And
Implementation code for "From Human Pose to On Body Devices for Human Activity Recognition.
Updating in progress.
The implementation is done in Python:
- torch
- numpy
- PyQt5
LARa dataset can be downloaded in https://zenodo.org/record/3862782#.XtVJOy9h3UI
Running the main.py
script in Annotation_Tool_LARa.
- For using the tCNNs for predicting activities classes, download the 'class_network.pt' and 'attr_network.pt' from LARa dataset.
- Store the networks 'class_network.pt' and 'attr_network.pt' in Annotation_Tool_LARa/networks/ Annotation_Tool_LARa/networks/class_network.pt Annotation_Tool_LARa/networks/attr_network.pt
- Erik Altermann erik.altermann@tu-dortmund.de
- Fernando Moya Rueda fernando.moya@tu-dortmund.de
Technische University of Dortmund Department of Computer Science Dortmund, Germany
The work on this publication was supported by Deutsche Forschungsgemeinschaft (DFG) in the context of the project Fi799/10-2, HO2403/14-2 ''Transfer Learning for Human Activity Recognition in Logistics''.
Annotation_Tool_LARa